Springer Science and Business Media LLC

Myelodysplastic syndromes (MDS) and acute myeloid leukaemia (AML) are characterised by abnormal epigenetic repression and differentiation of bone marrow haematopoietic stem cells (HSCs). Drugs that reverse epigenetic repression, such as 5-azacytidine (5-AZA), induce haematological improvement in half of treated patients. Although the mechanisms underlying therapy success are not yet clear, induction of endogenous retroelements (EREs) has been hypothesised. Using RNA sequencing (RNA-seq), we compared the transcription of EREs in bone marrow HSCs from a new cohort of MDS and chronic myelomonocytic leukaemia (CMML) patients before and after 5-AZA treatment with HSCs from healthy donors and AML patients. We further examined ERE transcription using the most comprehensive annotation of ERE-overlapping transcripts expressed in HSCs, generated here by de novo transcript assembly and supported by full-length RNA-seq. Consistent with prior reports, we found that treatment with 5-AZA increased the representation of ERE-derived RNA-seq reads in the transcriptome. However, such increases were comparable between treatment responses and failures. The extended view of HSC transcriptional diversity offered by de novo transcript assembly argued against 5-AZA-responsive EREs as determinants of the outcome of therapy. Instead, it uncovered pre-treatment expression and alternative splicing of developmentally regulated gene transcripts as predictors of the response of MDS and CMML patients to 5-AZA treatment. Our study identifies the developmentally regulated transcriptional signatures of protein-coding and non-coding genes, rather than EREs, as correlates of a favourable response of MDS and CMML patients to 5-AZA treatment and offers novel candidates for further evaluation.

The design of effective antimicrobial therapies for serious eukaryotic pathogens requires a clear understanding of their highly variable genomes. To facilitate analysis of copy number variations, single nucleotide polymorphisms and loss of heterozygosity events in these pathogens, we developed a pipeline for analyzing diverse genome-scale datasets from microarray, deep sequencing, and restriction site associated DNA sequence experiments for clinical and laboratory strains of Candida albicans, the most prevalent human fungal pathogen. The YMAP pipeline ( http://lovelace.cs.umn.edu/Ymap/ ) automatically illustrates genome-wide information in a single intuitive figure and is readily modified for the analysis of other pathogens with small genomes.

Patients are beginning to present to healthcare providers with the results of high-throughput individualized genotyping, and interpreting these results in the context of the explosive growth of literature linking individual variants with disease may seem daunting. However, we suggest that results of a personal genomic analysis may be viewed as a panel of many tests for multiple diseases. By using well-established methods of evidence based medicine, these very many parallel tests may be combined using likelihood ratios to report a post-test probability of disease for use in patient assessment.

Alzheimer’s disease (AD) is characterized by the intra- and extracellular accumulation of amyloid-β (Aβ) peptides. How Aβ aggregates perturb the proteome in brains of patients and AD transgenic mouse models, remains largely unclear. State-of-the-art mass spectrometry (MS) methods can comprehensively detect proteomic alterations, providing relevant insights unobtainable with transcriptomics investigations. Analyses of the relationship between progressive Aβ aggregation and protein abundance changes in brains of 5xFAD transgenic mice have not been reported previously. We quantified progressive Aβ aggregation in hippocampus and cortex of 5xFAD mice and controls with immunohistochemistry and membrane filter assays. Protein changes in different mouse tissues were analyzed by MS-based proteomics using label-free quantification; resulting MS data were processed using an established pipeline. Results were contrasted with existing proteomic data sets from postmortem AD patient brains. Finally, abundance changes in the candidate marker Arl8b were validated in cerebrospinal fluid (CSF) from AD patients and controls using ELISAs. Experiments revealed faster accumulation of Aβ42 peptides in hippocampus than in cortex of 5xFAD mice, with more protein abundance changes in hippocampus, indicating that Aβ42 aggregate deposition is associated with brain region-specific proteome perturbations. Generating time-resolved data sets, we defined Aβ aggregate-correlated and anticorrelated proteome changes, a fraction of which was conserved in postmortem AD patient brain tissue, suggesting that proteome changes in 5xFAD mice mimic disease-relevant changes in human AD. We detected a positive correlation between Aβ42 aggregate deposition in the hippocampus of 5xFAD mice and the abundance of the lysosome-associated small GTPase Arl8b, which accumulated together with axonal lysosomal membranes in close proximity of extracellular Aβ plaques in 5xFAD brains. Abnormal aggregation of Arl8b was observed in human AD brain tissue. Arl8b protein levels were significantly increased in CSF of AD patients. We report a comprehensive biochemical and proteomic investigation of hippocampal and cortical brain tissue derived from 5xFAD transgenic mice, providing a valuable resource to the neuroscientific community. We identified Arl8b, with significant abundance changes in 5xFAD and AD patient brains. Arl8b might enable the measurement of progressive lysosome accumulation in AD patients and have clinical utility as a candidate biomarker.

Expression quantitative trait locus analysis has emerged as an important component of efforts to understand how genetic polymorphisms influence disease risk and is poised to make contributions to translational medicine. Here we review how expression quantitative trait locus analysis is aiding the identification of which gene(s) within regions of association are causal for a disease or phenotypic trait; the narrowing down of the cell types or regulators involved in the etiology of disease; the characterization of drivers and modifiers of cancer; and our understanding of how different environments and cellular contexts can modify gene expression. We also introduce the concept of transcriptional risk scores as a means of refining estimates of individual liability to disease based on targeted profiling of the transcripts that are regulated by polymorphisms jointly associated with disease and gene expression.

Non-invasive prenatal diagnosis (NIPD) has substantial medical importance as it targets the development of safer and more effective methods to avoid the risk of fetal loss associated with currently used invasive methods. Several approaches have been demonstrated as being proof-of concept for NIPD of chromosomal aneuploidies. These approaches include cell-based and cell-free detection methods, involving the investigation of fetal cells in the maternal circulation, formaldehyde treatment of maternal plasma, DNA methylation studies using sodium bisulfite or restriction enzymes, protein-based studies, identification of fetal-specific mRNAs and digital polymerase chain reaction (PCR) approaches, and recently next-generation sequencing and methylated DNA immunoprecipitation real-time quantitative PCR-based approaches. Although all these NIPD methods have both advantages and limitations, some are moving closer to clinical implementation. Biotechnology companies dedicated to the development of NIPD tests such as the sequencing- or methylation-based approaches are finalizing large clinical trials. It is expected that these new technologies will facilitate safer, more sensitive and accurate prenatal diagnostic tests in the near future. In this review, we highlight the most recent advances in methods for NIPD of aneuploidies, and we discuss their future implications in clinical practice.

Những tiến bộ gần đây trong việc hiểu biết về hệ gen của metabolome con người đã làm sáng tỏ các con đường liên quan đến bệnh chuyển hóa và tim mạch. Các nghiên cứu như vậy phụ thuộc vào việc giải thích các quang phổ phân tử phức tạp. Một nghiên cứu gần đây của Suhre và các đồng nghiệp cung cấp một cách để xác định các chỉ số sinh học có khả năng liên quan đến lâm sàng mà không cần thông tin a priori, chẳng hạn như quang phổ tham khảo, từ đó giúp phát hiện thêm các đặc điểm quang phổ và các vùng gen tương ứng liên quan đến chuyển hóa và bệnh.

Spatial resolved transcriptomics (SRT) encompasses a rapidly developing set of technologies that enable the measurement of gene expression in tissue while retaining spatial localization information. SRT technologies and the enabled SRT studies have provided unprecedent insights into the structural and functional underpinnings of complex tissues. As SRT technologies have advanced and an increasing number of SRT studies have emerged, numerous sophisticated statistical and computational methods have been developed to facilitate the analysis and interpretation of SRT data. However, despite the growing popularity of SRT studies and the widespread availability of SRT analysis methods, analysis of large-scale and complex SRT datasets remains challenging and not easily accessible to researchers with limited statistical and computational backgrounds. Here, we present SRT-Server, the first webserver designed to carry out comprehensive SRT analyses for a wide variety of SRT technologies while requiring minimal prior computational knowledge. Implemented with cutting-edge web development technologies, SRT-Server is user-friendly and features multiple analytic modules that can perform a range of SRT analyses. With a flowchart-style interface, these different analytic modules on the SRT-Server can be dragged into the main panel and connected to each other to create custom analytic pipelines. SRT-Server then automatically executes the desired analyses, generates corresponding figures, and outputs results—all without requiring prior programming knowledge. We demonstrate the advantages of SRT-Server through three case studies utilizing SRT data collected from two common platforms, highlighting its versatility and values to researchers with varying analytic expertise. Overall, SRT-Server presents a user-friendly, efficient, effective, secure, and expandable solution for SRT data analysis, opening new doors for researchers in the field. SRT-Server is freely available at https://spatialtranscriptomicsanalysis.com/ .

Despite recent advances in research in hepatocarcinogenesis, we still lack a comprehensive view of the major pathways involved in liver carcinogenesis. Current concepts suggest that a limited number of molecular alterations involving oncogene activation and tumor suppressor inhibition are responsible for initiation of cancer. A recent publication by Zender et al. utilizes a combination of high-resolution comparative genomic hybridization, short hairpin RNA inhibition of target genes at the locations of focal genomic deletions, and a primed cell mosaic mouse model to identify novel tumor suppressors in hepatocellular carcinoma. This exciting new model promises to provide additional insights into the mechanisms of carcinogenesis.