Identification and validation of N-acetylputrescine in combination with non-canonical clinical features as a Parkinson's disease biomarker panel.

Parkinson's disease is a progressive neurodegenerative disorder in which loss of dopaminergic neurons in the substantia nigra results in a clinically heterogeneous group with variable motor and non-motor symptoms with a degree of misdiagnosis. Only 3-25% of sporadic Parkinson's patients present with genetic abnormalities that could represent a risk factor, thus environmental, metabolic, and other unknown causes contribute to the pathogenesis of Parkinson's disease, which highlights the critical need for biomarkers. In the present study, we prospectively collected and analyzed plasma samples from 194 Parkinson's disease patients and 197 age-matched non-diseased controls. N-acetyl putrescine (NAP) in combination with sense of smell (B-SIT), depression/anxiety (HADS), and acting out dreams (RBD1Q) clinical measurements demonstrated combined diagnostic utility. NAP was increased by 28% in Parkinsons disease patients and exhibited an AUC of 0.72 as well as an OR of 4.79. The clinical and NAP panel demonstrated an area under the curve, AUC = 0.9 and an OR of 20.4. The assessed diagnostic panel demonstrates combinatorial utility in diagnosing Parkinson's disease, allowing for an integrated interpretation of disease pathophysiology and highlighting the use of multi-tiered panels in neurological disease diagnosis.

Elevated levels of mitochondrial CoQ10 induce ROS-mediated apoptosis in pancreatic cancer.

Reactive oxygen species (ROS) are implicated in triggering cell signalling events and pathways to promote and maintain tumorigenicity. Chemotherapy and radiation can induce ROS to elicit cell death allows for targeting ROS pathways for effective anti-cancer therapeutics. Coenzyme Q10 is a critical cofactor in the electron transport chain with complex biological functions that extend beyond mitochondrial respiration. This study demonstrates that delivery of oxidized Coenzyme Q10 (ubidecarenone) to increase mitochondrial Q-pool is associated with an increase in ROS generation, effectuating anti-cancer effects in a pancreatic cancer model. Consequent activation of cell death was observed in vitro in pancreatic cancer cells, and both human patient-derived organoids and tumour xenografts. The study is a first to demonstrate the effectiveness of oxidized ubidecarenone in targeting mitochondrial function resulting in an anti-cancer effect. Furthermore, these findings support the clinical development of proprietary formulation, BPM31510, for treatment of cancers with high ROS burden with potential sensitivity to ubidecarenone.

An Empirical Antigen Selection Method Identifies Neoantigens That Either Elicit Broad Antitumor T-cell Responses or Drive Tumor Growth.

Neoantigens are critical targets of antitumor T-cell responses. The ATLAS bioassay was developed to identify neoantigens empirically by expressing each unique patient-specific tumor mutation individually in Escherichia coli, pulsing autologous dendritic cells in an ordered array, and testing the patient's T cells for recognition in an overnight assay. Profiling of T cells from patients with lung cancer revealed both stimulatory and inhibitory responses to individual neoantigens. In the murine B16F10 melanoma model, therapeutic immunization with ATLAS-identified stimulatory neoantigens protected animals, whereas immunization with peptides associated with inhibitory ATLAS responses resulted in accelerated tumor growth and abolished efficacy of an otherwise protective vaccine. A planned interim analysis of a clinical study testing a poly-ICLC adjuvanted personalized vaccine containing ATLAS-identified stimulatory neoantigens showed that it is well tolerated. In an adjuvant setting, immunized patients generated both CD4+ and CD8+ T-cell responses, with immune responses to 99% of the vaccinated peptide antigens. SIGNIFICANCE: Predicting neoantigens in silico has progressed, but empirical testing shows that T-cell responses are more nuanced than straightforward MHC antigen recognition. The ATLAS bioassay screens tumor mutations to uncover preexisting, patient-relevant neoantigen T-cell responses and reveals a new class of putatively deleterious responses that could affect cancer immunotherapy design.This article is highlighted in the In This Issue feature, p. 521.

A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit.

Early diagnosis of sepsis and septic shock has been unambiguously linked to lower mortality and better patient outcomes. Despite this, there is a strong unmet need for a reliable clinical tool that can be used for large-scale automated screening to identify high-risk patients. We addressed the following questions: Can a novel algorithm to identify patients at high risk of septic shock 24 hours before diagnosis be discovered using available clinical data? What are performance characteristics of this predictive algorithm? Can current metrics for evaluation of sepsis be improved using novel algorithm? Publicly available data from the intensive care unit setting was used to build septic shock and control patient cohorts. Using Bayesian networks, causal relationships between diagnosis groups, procedure groups, laboratory results, and demographic data were inferred. Predictive model for septic shock 24 hours prior to digital diagnosis was built based on inferred causal networks. Sepsis risk scores were augmented by de novo inferred model and performance was evaluated. A novel predictive model to identify high-risk patients 24 hours ahead of time, with area under curve of 0.81, negative predictive value of 0.87, and a positive predictive value as high as 0.65 was built. The specificity of quick sequential organ failure assessment, systemic inflammatory response syndrome, and modified early warning score was improved when augmented with the novel model, whereas no improvements were made to the sequential organ failure assessment score. We used a data-driven, expert knowledge agnostic method to build a screening algorithm for early detection of septic shock. The model demonstrates strong performance in the data set used and provides a basis for expanding this work toward building an algorithm that is used to screen patients based on electronic medical record data in real time.

Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data.

OBJECTIVE: Given the availability of extensive digitized healthcare data from medical records, claims and prescription information, it is now possible to use hypothesis-free, data-driven approaches to mine medical databases for novel insight. The goal of this analysis was to demonstrate the use of artificial intelligence based methods such as Bayesian networks to open up opportunities for creation of new knowledge in management of chronic conditions. MATERIALS AND METHODS: Hospital level Medicare claims data containing discharge numbers for most common diagnoses were analyzed in a hypothesis-free manner using Bayesian networks learning methodology. RESULTS: While many interactions identified between discharge rates of diagnoses using this data set are supported by current medical knowledge, a novel interaction linking asthma and renal failure was discovered. This interaction is non-obvious and had not been looked at by the research and clinical communities in epidemiological or clinical data. A plausible pharmacological explanation of this link is proposed together with a verification of the risk significance by conventional statistical analysis. CONCLUSION: Potential clinical and molecular pathways defining the relationship between commonly used asthma medications and renal disease are discussed. The study underscores the need for further epidemiological research to validate this novel hypothesis. Validation will lead to advancement in clinical treatment of asthma & bronchitis, thereby, improving patient outcomes and leading to long term cost savings. In summary, this study demonstrates that application of advanced artificial intelligence methods in healthcare has the potential to enhance the quality of care by discovering non-obvious, clinically relevant relationships and enabling timely care intervention.

Inference of transposable element ancestry.

Most common methods for inferring transposable element (TE) evolutionary relationships are based on dividing TEs into subfamilies using shared diagnostic nucleotides. Although originally justified based on the "master gene" model of TE evolution, computational and experimental work indicates that many of the subfamilies generated by these methods contain multiple source elements. This implies that subfamily-based methods give an incomplete picture of TE relationships. Studies on selection, functional exaptation, and predictions of horizontal transfer may all be affected. Here, we develop a Bayesian method for inferring TE ancestry that gives the probability that each sequence was replicative, its frequency of replication, and the probability that each extant TE sequence came from each possible ancestral sequence. Applying our method to 986 members of the newly-discovered LAVA family of TEs, we show that there were far more source elements in the history of LAVA expansion than subfamilies identified using the CoSeg subfamily-classification program. We also identify multiple replicative elements in the AluSc subfamily in humans. Our results strongly indicate that a reassessment of subfamily structures is necessary to obtain accurate estimates of mutation processes, phylogenetic relationships and historical times of activity.

Discovery of highly divergent repeat landscapes in snake genomes using high-throughput sequencing.

We conducted a comprehensive assessment of genomic repeat content in two snake genomes, the venomous copperhead (Agkistrodon contortrix) and the Burmese python (Python molurus bivittatus). These two genomes are both relatively small (∼1.4 Gb) but have surprisingly extensive differences in the abundance and expansion histories of their repeat elements. In the python, the readily identifiable repeat element content is low (21%), similar to bird genomes, whereas that of the copperhead is higher (45%), similar to mammalian genomes. The copperhead's greater repeat content arises from the recent expansion of many different microsatellites and transposable element (TE) families, and the copperhead had 23-fold greater levels of TE-related transcripts than the python. This suggests the possibility that greater TE activity in the copperhead is ongoing. Expansion of CR1 LINEs in the copperhead genome has resulted in TE-mediated microsatellite expansion ("microsatellite seeding") at a scale several orders of magnitude greater than previously observed in vertebrates. Snakes also appear to be prone to horizontal transfer of TEs, particularly in the copperhead lineage. The reason that the copperhead has such a small genome in the face of so much recent expansion of repeat elements remains an open question, although selective pressure related to extreme metabolic performance is an obvious candidate. TE activity can affect gene regulation as well as rates of recombination and gene duplication, and it is therefore possible that TE activity played a role in the evolution of major adaptations in snakes; some evidence suggests this may include the evolution of venom repertoires.