In-depth comparative analysis of Illumina® MiSeq run metrics: Development of a wet-lab quality assessment tool

Therapeutic avenues in bone repair: Harnessing an anabolic osteopeptide, PEPITEM, to boost bone growth and prevent bone loss

The existing suite of therapies for bone diseases largely act to prevent further bone loss but fail to stimulate healthy bone formation and repair. We describe an endogenous osteopeptide (PEPITEM) with anabolic osteogenic activity, regulating bone remodeling in health and disease. PEPITEM acts directly on osteoblasts through NCAM-1 signaling to promote their maturation and formation of new bone, leading to enhanced trabecular bone growth and strength. Simultaneously, PEPITEM stimulates an inhibitory paracrine loop: promoting osteoblast release of the decoy receptor osteoprotegerin, which sequesters RANKL, thereby limiting osteoclast activity and bone resorption. In disease models, PEPITEM therapy halts osteoporosis-induced bone loss and arthritis-induced bone damage in mice and stimulates new bone formation in osteoblasts derived from patient samples. Thus, PEPITEM offers an alternative therapeutic option in the management of diseases with excessive bone loss, promoting an endogenous anabolic pathway to induce bone remodeling and redress the imbalance in bone turnover.

Diversity of Endophytes of Actinidia arguta in Different Seasons

The seasonal changes in environmental conditions can alter the growth states of host plants, thereby affecting the living environment of endophytes and forming different endophytic communities. This study employs Illumina MiSeq next-generation sequencing to analyze the 16SrRNA and ITS rDNA of endophytes in 24 samples of Actinidia arguta stem tissues across different seasons. The results revealed a high richness and diversity of endophytes in Actinidia arguta, with significant seasonal variations in microbial community richness. This study identified 897 genera across 36 phyla for bacteria and 251 genera across 8 phyla for fungi. Notably, 69 bacterial genera and 19 fungal genera significantly contributed to the differences in community structure across seasons. A distinctive feature of coexistence in the endophytic community, both specific and conservative across different seasons, was observed. The bacterial community in winter demonstrated significantly higher richness and diversity compared to the other seasons. Environmental factors likely influence the optimal timing for endophyte colonization. Solar radiation, temperature, precipitation, and relative humidity significantly impact the diversity of endophytic bacteria and fungi. In addition, seasonal variations show significant differences in the nutritional modes of fungal endophytes and the degradation, ligninolysis, and ureolysis functions of bacterial endophytes. This study elucidates the potential role of endophytes in assisting Actinidia arguta in adapting to seasonal changes and provides a theoretical basis for further exploration of functional microbial strains.

Whole-Genome Sequence Analysis of Candida glabrata Isolates from a Patient with Persistent Fungemia and Determination of the Molecular Mechanisms of Multidrug Resistance

Whole-genome sequencing (WGS) was used to determine the molecular mechanisms of multidrug resistance for 10 serial Candida glabrata bloodstream isolates obtained from a neutropenic patient during 82 days of amphotericin B (AMB) or echinocandin therapy. For WGS, a library was prepared and sequenced using a Nextera DNA Flex Kit (Illumina) and the MiseqDx (Illumina) instrument. All isolates harbored the same Msh2p substitution, V239L, associated with multilocus sequence type 7 and a Pdr1p substitution, L825P, that caused azole resistance. Of six isolates with increased AMB MICs (≥2 mg/L), three harboring the Erg6p A158fs mutation had AMB MICs ≥ 8 mg/L, and three harboring the Erg6p R314K, Erg3p G236D, or Erg3p F226fs mutation had AMB MICs of 2-3 mg/L. Four isolates harboring the Erg6p A158fs or R314K mutation had fluconazole MICs of 4-8 mg/L while the remaining six had fluconazole MICs ≥ 256 mg/L. Two isolates with micafungin MICs > 8 mg/L harbored Fks2p (I661_L662insF) and Fks1p (C499fs) mutations, while six isolates with micafungin MICs of 0.25-2 mg/L harbored an Fks2p K1357E substitution. Using WGS, we detected novel mechanisms of AMB and echinocandin resistance; we explored mechanisms that may explain the complex relationship between AMB and azole resistance.

Evaluating the Transition from Targeted to Exome Sequencing: A Guide for Clinical Laboratories

The transition from targeted to exome or genome sequencing in clinical contexts requires quality standards, such as targeted sequencing, in order to be fully adopted. However, no clear recommendations or methodology have emerged for evaluating this technological evolution. We developed a structured method based on four run-specific sequencing metrics and seven sample-specific sequencing metrics for evaluating the performance of exome sequencing strategies to replace targeted strategies. The indicators include quality metrics and coverage performance on gene panels and OMIM morbid genes. We applied this general strategy to three different exome kits and compared them with a myopathy-targeted sequencing method. After having achieved 80 million reads, all-tested exome kits generated data suitable for clinical diagnosis. However, significant differences in the coverage and PCR duplicates were observed between the kits. These are two main criteria to consider for the initial implementation with high-quality assurance. This study aims to assist molecular diagnostic laboratories in adopting and evaluating exome sequencing kits in a diagnostic context compared to the strategy used previously. A similar strategy could be used to implement whole-genome sequencing for diagnostic purposes.

Diagnostic Validation of a Clinical Laboratory-Oriented Targeted RNA Sequencing System for Detecting Gene Fusions in Hematologic Malignancies

Targeted RNA sequencing (RNA-seq) is a highly accurate method for sequencing transcripts of interest with a high resolution and throughput. However, RNA-seq has not been widely performed in clinical molecular laboratories because of the complexity of data processing and interpretation. We developed and validated a customized RNA-seq panel and data processing protocol for fusion detection using 4 analytical validation samples and 51 clinical samples, covering seven types of hematologic malignancies. Analytical validation showed that the results for target gene coverage and between- and within-run precision and linearity tests were reliable. Using clinical samples, RNA-seq based on filtering and prioritization strategies detected all 25 known fusions previously found by multiplex reverse transcriptase-PCR and fluorescence in situ hybridization. It also detected nine novel fusions. Known fusions detected by RNA-seq included two IGH rearrangements supported by expression analysis. Novel fusions included six that targeted just one partner gene. In addition, 18 disease- and drug resistance-associated transcript variants in ABL1, GATA2, IKZF1, JAK2, RUNX1, and WT1 were designated simultaneously. Expression analysis showed distinct clustering according to subtype and lineage. In conclusion, this study showed that our customized RNA-seq system had a reliable and stable performance for fusion detection, with enhanced diagnostic yield for hematologic malignancies in a clinical diagnostic setting.

In-depth comparative analysis of Illumina® MiSeq run metrics: Development of a wet-lab quality assessment tool

Whole genome sequencing of bacterial isolates has become a daily task in many laboratories, generating incredible amounts of data. However, data acquisition is not an end in itself; the goal is to acquire high-quality data useful for understanding genetic relationships. Having a method that could rapidly determine which of the many available run metrics are the most important indicators of overall run quality and having a way to monitor these during a given sequencing run would be extremely helpful to this effect. Therefore, we compared various run metrics across 486 MiSeq runs, from five different machines. By performing a statistical analysis using principal components analysis and a K-means clustering algorithm of the metrics, we were able to validate metric comparisons among instruments, allowing for the development of a predictive algorithm, which permits one to observe whether a given MiSeq run has performed adequately. This algorithm is available in an Excel spreadsheet: that is, MiSeq Instrument & Run (In-Run) Forecast. Our tool can help verify that the quantity/quality of the generated sequencing data consistently meets or exceeds recommended manufacturer expectations. Patterns of deviation from those expectations can be used to assess potential run problems and plan preventative maintenance, which can save valuable time and funding resources.