Circulating cell-free DNA as a biomarker for diagnosis of Schistosomiasis japonica.

BACKGROUND: Schistosomiasis, a neglected tropical disease, remains an important public health problem. Although there are various methods for diagnosing schistosomiasis, many limitations still exist. Early diagnosis and treatment of schistosomiasis can significantly improve survival and prognosis of patients. METHODOLOGY: Circulating cell-free (cf)DNA has been widely used in the diagnosis of various diseases. In our study, we evaluated the diagnostic value of circulating cfDNA for schistosomiasis caused by Schistosoma japonicum. We focused on the tandem sequences and mitochondrial genes of S. japonicum to identify highly sensitive and specific targets for diagnosis of Schistosomiasis japonica. RESULTS: Through data screening and analysis, we ultimately identified four specific tandem sequences (TD-1, TD-2, TD-3. and TD-4) and six mitochondrial genes (COX1(1), COX1(2), CYTB, ATP6, COX3, and ND5). We designed specific primers to detect the amount of circulating cfDNA in S. japonicum-infected mouse and chronic schistosomiasis patients. Our results showed that the number of tandem sequences was significantly higher than that of the mitochondrial genes. A S. japonicum infection model in mice suggested that infection of S. japonicum can be diagnosed by detecting circulating cfDNA as early as the first week. We measured the expression levels of circulating cfDNA (TD-1, TD-2, and TD-3) at different time points and found that TD-3 expression was significantly higher than that of TD-1 or TD-2. We also infected mice with different quantities of cercariae (20 s and 80 s). The level of cfDNA (TD-3) in the 80 s infection group was significantly higher than in the 20 s infection group. Additionally, cfDNA (TD-3) levels increased after egg deposition. Meanwhile, we tested 42 patients with chronic Schistosomiasis japonica and circulating cfDNA (TD-3) was detected in nine patients. CONCLUSIONS: We have screened highly sensitive targets for the diagnosis of Schistosomiasis japonica, and the detection of circulating cfDNA is a rapid and effective method for the diagnosis of Schistosomiasis japonica. The levels of cfDNA is correlated with cercariae infection severity. Early detection and diagnosis of schistosomiasis is crucial for patient treatment and improving prognosis.

Comparative analysis of clinical and immunological profiles across Omicron BA.5.2 subvariants using next-generation sequencing in a Chinese cohort.

Objective: The Omicron BA.5.2 variant of SARS-CoV-2 has undergone several evolutionary adaptations, leading to multiple subvariants. Rapid and accurate characterization of these subvariants is essential for effective treatment, particularly in critically ill patients. This study leverages Next-Generation Sequencing (NGS) to elucidate the clinical and immunological features across different Omicron BA.5.2 subvariants. Methods: We enrolled 28 patients infected with the Omicron variant, hospitalized in Zhangjiajie People's Hospital, Hunan, China, between January 20, 2023, and March 31, 2023. Throat swabs were collected upon admission for NGS-based identification of Omicron subvariants. Clinical data, including qSOFA scores and key laboratory tests, were collated. A detailed analysis of lymphocyte subsets was conducted to ascertain the extent of immune cell damage and disease severity. Results: Patients were infected with various Omicron subvariants, including BA.5.2.48, BA.5.2.49, BA.5.2.6, BF.7.14, DY.1, DY.2, DY.3, and DY.4. Despite having 43 identical mutation sites, each subvariant exhibited unique marker mutations. Critically ill patients demonstrated significant depletion in total lymphocyte count, T cells, CD4, CD8, B cells, and NK cells (P < 0.05). However, there were no significant differences in clinical and immunological markers across the subvariants. Conclusion: This study reveals that critically ill patients infected with different Omicron BA.5.2 subvariants experience similar levels of cellular immune dysfunction and inflammatory response. Four mutations - ORF1a:K3353R, ORF1a:L3667F, ORF1b:S997P, S:T883I showed correlation with immunological responses although this conclusion suffers from the small sample size. Our findings underscore the utility of NGS in the comprehensive assessment of infectious diseases, contributing to more effective clinical decision-making.

Application of metagenomic next-generation sequencing in the diagnosis and treatment guidance of Pneumocystis jirovecii pneumonia in renal transplant recipients.

Pneumocystis jirovecii pneumonia (PJP) is difficult to be diagnosed, so this study explored if PJP could be diagnosed by metagenomic next-generation sequencing (mNGS) and if mNGS could guide the therapy of PJP. mNGS was successfully diagnosed 13 out of 14 PJP recipients with 11 through peripheral blood samples, verified by PCR. Ten non-PJP recipients were enrolled as the control group. Blood tests revealed a high ß-D-glucan (BDG) level in all recipients with PJP during the hospitalization. Four (28.6%) of 14 PJP patients were infected with cytomegalovirus simultaneously, while 8 (57.1%) suffered from a combined infection caused by Torque teno virus. Five (35.7%) of 14 cases died of PJP or the subsequent bacteremias/bacterial pneumonia with a longer interval between the onset and diagnosis of/the available therapy against PJP than survival cases. Univariate analysis of characteristics between PJP and non-PJP recipients revealed that BDG assays was higher at the admission in PJP group (P =0.011). This present study supports the value of mNGS detection of blood sample in diagnosing PJP, which could assist clinical decision for therapy against PJ and improve outcome of PJP. The study also highlights the sensitivity of BDG assays. Cytomegalovirus and Torque teno virus infections often occur at the same time of PJP, thus can be alerts of PJP.

Genomic data analysis workflows for tumors from patient-derived xenografts (PDXs): challenges and guidelines.

BACKGROUND: Patient-derived xenograft (PDX) models are in vivo models of human cancer that have been used for translational cancer research and therapy selection for individual patients. The Jackson Laboratory (JAX) PDX resource comprises 455 models originating from 34 different primary sites (as of 05/08/2019). The models undergo rigorous quality control and are genomically characterized to identify somatic mutations, copy number alterations, and transcriptional profiles. Bioinformatics workflows for analyzing genomic data obtained from human tumors engrafted in a mouse host (i.e., Patient-Derived Xenografts; PDXs) must address challenges such as discriminating between mouse and human sequence reads and accurately identifying somatic mutations and copy number alterations when paired non-tumor DNA from the patient is not available for comparison. RESULTS: We report here data analysis workflows and guidelines that address these challenges and achieve reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from PDX models that lack genomic data from paired non-tumor tissue for comparison. Our workflows incorporate commonly used software and public databases but are tailored to address the specific challenges of PDX genomics data analysis through parameter tuning and customized data filters and result in improved accuracy for the detection of somatic alterations in PDX models. We also report a gene expression-based classifier that can identify EBV-transformed tumors. We validated our analytical approaches using data simulations and demonstrated the overall concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA). CONCLUSIONS: The analysis workflows that we have developed to accurately predict somatic profiles of tumors from PDX models that lack normal tissue for comparison enable the identification of the key oncogenic genomic and expression signatures to support model selection and/or biomarker development in therapeutic studies. A reference implementation of our analysis recommendations is available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows .

The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies.

BACKGROUND: Precision medicine in oncology relies on rapid associations between patient-specific variations and targeted therapeutic efficacy. Due to the advancement of genomic analysis, a vast literature characterizing cancer-associated molecular aberrations and relative therapeutic relevance has been published. However, data are not uniformly reported or readily available, and accessing relevant information in a clinically acceptable time-frame is a daunting proposition, hampering connections between patients and appropriate therapeutic options. One important therapeutic avenue for oncology patients is through clinical trials. Accordingly, a global view into the availability of targeted clinical trials would provide insight into strengths and weaknesses and potentially enable research focus. However, data regarding the landscape of clinical trials in oncology is not readily available, and as a result, a comprehensive understanding of clinical trial availability is difficult. RESULTS: To support clinical decision-making, we have developed a data loader and mapper that connects sequence information from oncology patients to data stored in an in-house database, the JAX Clinical Knowledgebase (JAX-CKB), which can be queried readily to access comprehensive data for clinical reporting via customized reporting queries. JAX-CKB functions as a repository to house expertly curated clinically relevant data surrounding our 358-gene panel, the JAX Cancer Treatment Profile (JAX CTP), and supports annotation of functional significance of molecular variants. Through queries of data housed in JAX-CKB, we have analyzed the landscape of clinical trials relevant to our 358-gene targeted sequencing panel to evaluate strengths and weaknesses in current molecular targeting in oncology. Through this analysis, we have identified patient indications, molecular aberrations, and targeted therapy classes that have strong or weak representation in clinical trials. CONCLUSIONS: Here, we describe the development and disseminate system methods for associating patient genomic sequence data with clinically relevant information, facilitating interpretation and providing a mechanism for informing therapeutic decision-making. Additionally, through customized queries, we have the capability to rapidly analyze the landscape of targeted therapies in clinical trials, enabling a unique view into current therapeutic availability in oncology.

Exome sequencing reveals pathogenic mutations in 91 strains of mice with Mendelian disorders.

Spontaneously arising mouse mutations have served as the foundation for understanding gene function for more than 100 years. We have used exome sequencing in an effort to identify the causative mutations for 172 distinct, spontaneously arising mouse models of Mendelian disorders, including a broad range of clinically relevant phenotypes. To analyze the resulting data, we developed an analytics pipeline that is optimized for mouse exome data and a variation database that allows for reproducible, user-defined data mining as well as nomination of mutation candidates through knowledge-based integration of sample and variant data. Using these new tools, putative pathogenic mutations were identified for 91 (53%) of the strains in our study. Despite the increased power offered by potentially unlimited pedigrees and controlled breeding, about half of our exome cases remained unsolved. Using a combination of manual analyses of exome alignments and whole-genome sequencing, we provide evidence that a large fraction of unsolved exome cases have underlying structural mutations. This result directly informs efforts to investigate the similar proportion of apparently Mendelian human phenotypes that are recalcitrant to exome sequencing.