The impact of maceration on the ‘Osteo-ome’; a pilot investigation

The bone proteome, i.e., the ‘osteo-ome’, is a rich source of information for forensic studies. There have been advances in the study of biomolecule biomarkers for age-at-death (AAD) and post-mortem interval (PMI) estimations, by looking at changes in protein abundance and post-translational modifications (PTMs) at the peptide level. However, the extent to which other post-mortem factors alter the proteome, including ‘maceration’ procedures adopted in human taphonomy facilities (HTFs) to clean bones for osteological collections, is poorly understood. This pilot study aimed to characterise the impact of these ‘cleaning’ methods for de-fleshing skeletons on bone biomolecules, and therefore, what further impact this may have on putative biomarkers in future investigations. Three specific maceration procedures, varying in submersion time (one week or two days) and water temperature (55 °C or 87 °C) were conducted on six bovid tibiae from three individual bovines; the proteome of fresh and macerated bones of each individual was compared. The maceration at 87 °C for two days had the greatest proteomic impact, decreasing protein relative abundances and inducing specific PTMs. Overall, these results suggest that routinely-employed maceration procedures are harsh, variable and potentially threaten the viability of discovering new forensic biomarkers in macerated skeletal remains

Integration of genome-level data to allow identification of subtype-specific vulnerability genes as novel therapeutic targets

The identification of cancer-specific vulnerability genes is one of the most promising approaches for developing more effective and less toxic cancer treatments. Cancer genomes exhibit thousands of changes in DNA methylation and gene expression, with the vast majority likely to be passenger changes. We hypothesised that, through integration of genome-wide DNA methylation/expression data, we could exploit this inherent variability to identify cancer subtype-specific vulnerability genes that would represent novel therapeutic targets that could allow cancer-specific cell killing. We developed a bioinformatics pipeline integrating genome-wide DNA methylation/gene expression data to identify candidate subtype-specific vulnerability partner genes for the genetic drivers of individual genetic/molecular subtypes. Using acute lymphoblastic leukaemia as an initial model, 21 candidate subtype-specific vulnerability genes were identified across the five common genetic subtypes, with at least one per subtype. To confirm the approach was applicable across cancer types, we also assessed medulloblastoma, identifying 15 candidate subtype-specific vulnerability genes across three of four established subtypes. Almost all identified genes had not previously been implicated in these diseases. Functional analysis of seven candidate subtype-specific vulnerability genes across the two tumour types confirmed that siRNA-mediated knockdown induced significant inhibition of proliferation/induction of apoptosis, which was specific to the cancer subtype in which the gene was predicted to be specifically lethal. Thus, we present a novel approach that integrates genome-wide DNA methylation/expression data to identify cancer subtype-specific vulnerability genes as novel therapeutic targets. We demonstrate this approach is applicable to multiple cancer types and identifies true functional subtype-specific vulnerability genes with high efficiency

Bone Proteomics Method Optimization for Forensic Investigations

The application of proteomic analysis to forensic skeletal remains has gained significant interest in improving biological and chronological estimations in medico-legal investigations. To enhance the applicability of these analyses to forensic casework, it is crucial to maximize throughput and proteome recovery while minimizing interoperator variability and laboratory-induced post-translational protein modifications (PTMs). This work compared different workflows for extracting, purifying, and analyzing bone proteins using liquid chromatography with tandem mass spectrometry (LC–MS)/MS including an in-StageTip protocol previously optimized for forensic applications and two protocols using novel suspension-trap technology (S-Trap) and different lysis solutions. This study also compared data-dependent acquisition (DDA) with data-independent acquisition (DIA). By testing all of the workflows on 30 human cortical tibiae samples, S-Trap workflows resulted in increased proteome recovery with both lysis solutions tested and in decreased levels of induced deamidations, and the DIA mode resulted in greater sensitivity and window of identification for the identification of lower-abundance proteins, especially when open-source software was utilized for data processing in both modes. The newly developed S-Trap protocol is, therefore, suitable for forensic bone proteomic workflows and, particularly when paired with DIA mode, can offer improved proteomic outcomes and increased reproducibility, showcasing its potential in forensic proteomics and contributing to achieving standardization in bone proteomic analyses for forensic applications

Pediatric pan-central nervous system tumor analysis of immune-cell infiltration identifies correlates of antitumor immunity

Here, using methylCIBERSORT, the authors characterize the tumour-immune microenvironment of paediatric central nervous system (CNS) tumours and its association with tumour type and prognosis. These findings suggest that immuno-methylomic profiling may inform immunotherapy approaches in paediatric patients with CNS tumour

Metabolite profiles of medulloblastoma for rapid and non-invasive detection of molecular disease groups

BackgroundThe malignant childhood brain tumour, medulloblastoma, is classified clinically into molecular groups which guide therapy. DNA-methylation profiling is the current classification ‘gold-standard’, typically delivered 3–4 weeks post-surgery. Pre-surgery non-invasive diagnostics thus offer significant potential to improve early diagnosis and clinical management. Here, we determine tumour metabolite profiles of the four medulloblastoma groups, assess their diagnostic utility using tumour tissue and potential for non-invasive diagnosis using in vivo magnetic resonance spectroscopy (MRS).MethodsMetabolite profiles were acquired by high-resolution magic-angle spinning NMR spectroscopy (MAS) from 86 medulloblastomas (from 59 male and 27 female patients), previously classified by DNA-methylation array (WNT (n = 9), SHH (n = 22), Group3 (n = 21), Group4 (n = 34)); RNA-seq data was available for sixty. Unsupervised class-discovery was performed and a support vector machine (SVM) constructed to assess diagnostic performance. The SVM classifier was adapted to use only metabolites (n = 10) routinely quantified from in vivo MRS data, and re-tested. Glutamate was assessed as a predictor of overall survival.FindingsGroup-specific metabolite profiles were identified; tumours clustered with good concordance to their reference molecular group (93%). GABA was only detected in WNT, taurine was low in SHH and lipids were high in Group3. The tissue-based metabolite SVM classifier had a cross-validated accuracy of 89% (100% for WNT) and, adapted to use metabolites routinely quantified in vivo, gave a combined classification accuracy of 90% for SHH, Group3 and Group4. Glutamate predicted survival after incorporating known risk-factors (HR = 3.39, 95% CI 1.4–8.1, p = 0.025).InterpretationTissue metabolite profiles characterise medulloblastoma molecular groups. Their combination with machine learning can aid rapid diagnosis from tissue and potentially in vivo. Specific metabolites provide important information; GABA identifying WNT and glutamate conferring poor prognosis

Constraints on cosmic strings using data from the first Advanced LIGO observing run

Cosmic strings are topological defects which can be formed in grand unified theory scale phase transitions in the early universe. They are also predicted to form in the context of string theory. The main mechanism for a network of Nambu-Goto cosmic strings to lose energy is through the production of loops and the subsequent emission of gravitational waves, thus offering an experimental signature for the existence of cosmic strings. Here we report on the analysis conducted to specifically search for gravitational-wave bursts from cosmic string loops in the data of Advanced LIGO 2015-2016 observing run (O1). No evidence of such signals was found in the data, and as a result we set upper limits on the cosmic string parameters for three recent loop distribution models. In this paper, we initially derive constraints on the string tension Gμ and the intercommutation probability, using not only the burst analysis performed on the O1 data set but also results from the previously published LIGO stochastic O1 analysis, pulsar timing arrays, cosmic microwave background and big-bang nucleosynthesis experiments. We show that these data sets are complementary in that they probe gravitational waves produced by cosmic string loops during very different epochs. Finally, we show that the data sets exclude large parts of the parameter space of the three loop distribution models we consider

Genomic analysis of endemic clones of toxigenic and non-toxigenic Corynebacterium diphtheriae in Belarus during and after the major epidemic in 1990s

Abstract Background Diphtheria remains a major public health concern with multiple recent outbreaks around the world. Moreover, invasive non-toxigenic strains have emerged globally causing severe infections. A diphtheria epidemic in the former Soviet Union in the 1990s resulted in ~5000 deaths. In this study, we analysed the genome sequences of a collection of 93 C. diphtheriae strains collected during and after this outbreak (1996 – 2014) in a former Soviet State, Belarus to understand the evolutionary dynamics and virulence capacities of these strains. Results C. diphtheriae strains from Belarus belong to ten sequence types (STs). Two major clones, non-toxigenic ST5 and toxigenic ST8, encompassed 76% of the isolates that are associated with sore throat and diphtheria in patients, respectively. Core genomic diversity is limited within outbreak-associated ST8 with relatively higher mutation rates (8.9 × 10−7 substitutions per strain per year) than ST5 (5.6 × 10−7 substitutions per strain per year) where most of the diversity was introduced by recombination. A variation in the virulence gene repertoire including the presence of tox gene is likely responsible for pathogenic differences between different strains. However, strains with similar virulence potential can cause disease in some individuals and remain asymptomatic in others. Eight synonymous single nucleotide polymorphisms were observed between the tox genes of the vaccine strain PW8 and other toxigenic strains of ST8, ST25, ST28, ST41 and non-toxigenic tox gene-bearing (NTTB) ST40 strains. A single nucleotide deletion at position 52 in the tox gene resulted in the frameshift in ST40 isolates, converting them into NTTB strains. Conclusions Non-toxigenic C. diphtheriae ST5 and toxigenic ST8 strains have been endemic in Belarus both during and after the epidemic in 1990s. A high vaccine coverage has effectively controlled diphtheria in Belarus; however, non-toxigenic strains continue to circulate in the population. Recombination is an important evolutionary force in shaping the genomic diversity in C. diphtheriae. However, the relative role of recombination and mutations in diversification varies between different clones