Validation of a Stenotrophomonas maltophilia bloodstream infection prediction score in the hematologic malignancy population.

Stenotrophomonas maltophilia (SM) bloodstream infections (BSIs) contribute to significant mortality in hematologic malignancy (HM) and hematopoietic stem cell transplantation (HSCT) patients. A risk score to predict SM BSI could reduce time to appropriate antimicrobial therapy (TTAT) and improve patient outcomes. A single center cohort study of hospitalized adults with HM/HSCT was conducted. Patients had ≥ 1 blood culture with a Gram-negative (GN) organism. A StenoSCORE was calculated for each patient. The StenoSCORE2 was developed using risk factors for SM BSI identified via logistic regression. Receiver operating characteristic (ROC) curves were plotted. Sensitivity and specificity for the StenoSCORE and StenoSCORE2 were calculated. Thirty-six SM patients and 534 non-SM patients were assessed. A StenoSCORE ≥ 33 points was 80% sensitive, 68% specific, and accurately classified 69% of GN BSIs. StenoSCORE2 variables included acute leukemia, prolonged neutropenia, mucositis, ICU admission, recent meropenem and/or cefepime exposure. The StenoSCORE2 performed better than the StenoSCORE (ROC AUC 0.84 vs. 0.77). A StenoSCORE2 ≥ 4 points was 86% sensitive, 76% specific, and accurately classified 77% of GN BSIs. TTAT was significantly longer for patients with SM BSI compared with non-SM BSI (45.16 h vs. 0.57 h; p < 0.0001). In-hospital and 28-day mortality were significantly higher for patients with SM BSI compared to non-SM BSI (58.3% vs. 18.5% and 66.7% vs. 26.4%; p-value < 0.0001). The StenoSCORE and StenoSCORE2 performed well in predicting SM BSIs in patients with HM/HSCT and GN BSI. Clinical studies evaluating whether StenoSCORE and/or StenoSCORE2 implementation improves TTAT and clinical outcomes are warranted.

Expanding Per- and Polyfluoroalkyl Substances Coverage in Nontargeted Analysis Using Data-Independent Analysis and IonDecon.

Per- and polyfluoroalkyl substances (PFAS) are widespread, persistent environmental contaminants that have been linked to various health issues. Comprehensive PFAS analysis often relies on ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC HRMS) and molecular fragmentation (MS/MS). However, the selection and fragmentation of ions for MS/MS analysis using data-dependent analysis results in only the topmost abundant ions being selected. To overcome these limitations, All Ions fragmentation (AIF) can be used alongside data-dependent analysis. In AIF, ions across the entire m/z range are simultaneously fragmented; hence, precursor-fragment relationships are lost, leading to a high false positive rate. We introduce IonDecon, which filters All Ions data to only those fragments correlating with precursor ions. This software can be used to deconvolute any All Ions files and generates an open source DDA formatted file, which can be used in any downstream nontargeted analysis workflow. In a neat solution, annotation of PFAS standards using IonDecon and All Ions had the exact same false positive rate as when using DDA; this suggests accurate annotation using All Ions and IonDecon. Furthermore, deconvoluted All Ions spectra retained the most abundant peaks also observed in DDA, while filtering out much of the artifact peaks. In complex samples, incorporating AIF and IonDecon into workflows can enhance the MS/MS coverage of PFAS (more than tripling the number of annotations in domestic sewage). Deconvolution in complex samples of All Ions data using IonDecon did retain some false fragments (fragments not observed when using ion selection, which were not isotopes or multimers), and therefore DDA and intelligent acquisition methods should still be acquired when possible alongside All Ions to decrease the false positive rate. Increased coverage of PFAS can inform on the development of regulations to address the entire PFAS problem, including both legacy and newly discovered PFAS.

Methodologies in Mitochondrial Testing: Diagnosing a Primary Mitochondrial Respiratory Chain Disorder.

BACKGROUND: Mitochondria are cytosolic organelles within most eukaryotic cells. Mitochondria generate the majority of cellular energy in the form of adenosine triphosphate (ATP) through oxidative phosphorylation (OxPhos). Pathogenic variants in mitochondrial DNA (mtDNA) and nuclear DNA (nDNA) lead to defects in OxPhos and physiological malfunctions (Nat Rev Dis Primer 2016;2:16080.). Patients with primary mitochondrial disorders (PMD) experience heterogeneous symptoms, typically in multiple organ systems, depending on the tissues affected by mitochondrial dysfunction. Because of this heterogeneity, clinical diagnosis is challenging (Annu Rev Genomics Hum Genet 2017;18:257-75.). Laboratory diagnosis of mitochondrial disease depends on a multipronged analysis that can include biochemical, histopathologic, and genetic testing. Each of these modalities has complementary strengths and limitations in diagnostic utility. CONTENT: The primary focus of this review is on diagnosis and testing strategies for primary mitochondrial diseases. We review tissue samples utilized for testing, metabolic signatures, histologic findings, and molecular testing approaches. We conclude with future perspectives on mitochondrial testing. SUMMARY: This review offers an overview of the current biochemical, histologic, and genetic approaches available for mitochondrial testing. For each we review their diagnostic utility including complementary strengths and weaknesses. We identify gaps in current testing and possible future avenues for test development.

Big Data Everywhere: The Impact of Data Disjunction in the Direct-to-Consumer Testing Model.

The recent increase in accessible medical and clinical laboratory "Big Data" has led to a corresponding increase in the use of machine-learning tools to develop integrative diagnostic models incorporating both existing and new test data. The rise of direct-to-consumer (DTC) testing paradigms raises the possibility of predictive models that use these new sources. This article discusses several distinct challenges raised by the DTC approach, including issues of centralized data collection, ascertainment bias, linkage to medical outcomes, and standardization/harmonization of results. Several solutions to maximize the promise of machine-learning data analytics for DTC data are suggested.

Ultrahigh-Performance Liquid Chromatography-High-Resolution Mass Spectrometry Metabolomics and Lipidomics Study of Stool from Transgenic Parkinson's Disease Mice Following Immunotherapy.

Parkinson's disease (PD) is characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta of the brain, as well as the degeneration of motor and nonmotor circuitries. The cause of neuronal death is currently unknown, although chronic neuroinflammation, aggregated α-synuclein, mitochondrial dysfunction, and oxidative stress have all been implicated. Gliosis has been shown to exacerbate neuroinflammation via secretion of proinflammatory cytokines, and there is a subsequent infiltration of T lymphocytes (T-cells), into the brain of PD patients. Using liquid chromatography-high-resolution mass spectrometry (LC-HRMS), we have observed metabolomic changes in stool samples, thought to be associated with the potential disease-modifying effect of immunotherapy administered to transgenic Parkinsonian (A53T) mice. Significant elevations (p < 0.05) in metabolites associated with immune response (taurine, histamine, and its methylated product, 3-methylhistamine) are identified as being higher in the mice undergoing immunotherapy. Furthermore, a reduction in triacylglycerol (TG) and diacylglycerol (DG) expressions in stool following immunotherapy suggests a regulation of lipid breakdown or biosynthesis with the vaccine. These "omics" markers (among others reported in this article) along with weight gain and increased life expectancy suggest that immunotherapy is positively modifying the disease state.

Mass Spectrometric Methodologies for Investigating the Metabolic Signatures of Parkinson's Disease: Current Progress and Future Perspectives.

Parkinson's disease (PD) is a neurodegenerative disorder resulting from the loss of dopaminergic neurons of the substantia nigra as well as degeneration of motor and nonmotor basal ganglia circuitries. Typically known for classical motor deficits (tremor, rigidity, bradykinesia), early stages of the disease are associated with a large nonmotor component (depression, anxiety, apathy, etc.). Currently, there are no definitive biomarkers of PD, and the measurement of dopamine metabolites does not allow for detection of prodromal PD nor does it aid in long-term monitoring of disease progression. Given that PD is increasingly recognized as complex and heterogeneous, involving several neurotransmitters and proteins, it is of importance that we advance interdisciplinary studies to further our knowledge of the molecular and cellular pathways that are affected in PD. This approach will possibly yield useful biomarkers for early diagnosis and may assist in the development of disease-modifying therapies. Here, we discuss preanalytical factors associated with metabolomics studies, summarize current mass spectrometric methodologies used to evaluate the metabolic signature of PD, and provide future perspectives of the rapidly developing field of MS in the context of PD.

l-Carnitine Inhibits Lipopolysaccharide-Induced Nitric Oxide Production of SIM-A9 Microglia Cells.

Microglia are the resident immune effector cells of the central nervous system. They account for approximately 10-15% of all cells found in the brain and spinal cord, acting as macrophages, sensing and engaging in phagocytosis to eliminate toxic proteins. Microglia are dynamic and can change their morphology in response to cues from their milieu. Parkinson's disease is a neurodegenerative disease, associated with reactive gliosis, neuroinflammation, and oxidative stress. It is thought that Parkinson's disease is caused by the accumulation of abnormally folded alpha-synuclein protein, accompanied by persistent neuroinflammation, oxidative stress, and subsequent neuronal injury/death. There is evidence in the literature for mitochondrial dysfunction in Parkinson's disease as well as fatty acid beta-oxidation, involving l-carnitine. Here we investigate l-carnitine in the context of microglial activation, suggesting a potential new strategy of supplementation for PD patients. Preliminary results from our studies suggest that the treatment of activated microglia with the endogenous antioxidant l-carnitine can reverse the effects of detrimental neuroinflammation in vitro.

Monitoring Dopamine ex Vivo during Electrical Stimulation Using Liquid-Microjunction Surface Sampling.

Liquid-microjunction surface sampling (LMJ-SS) is an ambient ionization technique based on the continuous flow of solvent using an in situ microextraction device in which solvent moves through the probe, drawing in the analytes in preparation for ionization using an electrospray ionization source. However, unlike traditional mass spectrometry (MS) techniques, it operates under ambient pressure and requires no sample preparation, thereby making it ideal for rapid sampling of thicker tissue sections for electrophysiological and other neuroscientific research studies. Studies interrogating neural synapses, or a specific neural circuit, typically employ thick, ex vivo tissue sections maintained under near-physiological conditions to preserve tissue viability and maintain the neural networks. Deep brain stimulation (DBS) is a surgical procedure used to treat the neurological symptoms that are associated with certain neurodegenerative and neuropsychiatric diseases. Parkinson's disease (PD) is a neurological disorder which is commonly treated with DBS therapy. PD is characterized by the degeneration of dopaminergic neurons in the substantia nigra pars compacta portion of the brain. Here, we demonstrate that the LMJ-SS methodology can provide a platform for ex vivo analysis of the brain during electrical stimulation, such as DBS. We employ LMJ-SS in the ex vivo analysis of mouse brain tissue for monitoring dopamine during electrical stimulation of the striatum region. The mouse brain tissue was sectioned fresh post sacrifice and maintained in artificial cerebrospinal fluid to create near-physiological conditions before direct sampling using LMJ-SS. A selection of metabolites, including time-sensitive metabolites involved in energy regulation in the brain, were identified using standards, and the mass spectral database mzCloud was used to assess the feasibility of the methodology. Thereafter, the intensity of m/z 154 corresponding to protonated dopamine was monitored before and after electrical stimulation of the striatum region, showing an increase in signal directly following a stimulation event. Dopamine is the key neurotransmitter implicated in PD, and although electrochemical detectors have shown such increases in dopamine post-DBS, this is the first study to do so using MS methodologies.

Expanding Lipidome Coverage Using LC-MS/MS Data-Dependent Acquisition with Automated Exclusion List Generation.

Untargeted omics analyses aim to comprehensively characterize biomolecules within a biological system. Changes in the presence or quantity of these biomolecules can indicate important biological perturbations, such as those caused by disease. With current technological advancements, the entire genome can now be sequenced; however, in the burgeoning fields of lipidomics, only a subset of lipids can be identified. The recent emergence of high resolution tandem mass spectrometry (HR-MS/MS), in combination with ultra-high performance liquid chromatography, has resulted in an increased coverage of the lipidome. Nevertheless, identifications from MS/MS are generally limited by the number of precursors that can be selected for fragmentation during chromatographic elution. Therefore, we developed the software IE-Omics to automate iterative exclusion (IE), where selected precursors using data-dependent topN analyses are excluded in sequential injections. In each sequential injection, unique precursors are fragmented until HR-MS/MS spectra of all ions above a user-defined intensity threshold are acquired. IE-Omics was applied to lipidomic analyses in Red Cross plasma and substantia nigra tissue. Coverage of the lipidome was drastically improved using IE. When applying IE-Omics to Red Cross plasma and substantia nigra lipid extracts in positive ion mode, 69% and 40% more molecular identifications were obtained, respectively. In addition, applying IE-Omics to a lipidomics workflow increased the coverage of trace species, including odd-chained and short-chained diacylglycerides and oxidized lipid species. By increasing the coverage of the lipidome, applying IE to a lipidomics workflow increases the probability of finding biomarkers and provides additional information for determining etiology of disease. Graphical Abstract ᅟ.

Precast Gelatin-Based Molds for Tissue Embedding Compatible with Mass Spectrometry Imaging.

Preparation of tissue for matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) generally involves embedding the tissue followed by freezing and cryosectioning, usually between 5 and 25 µm thick, depending on the tissue type and the analyte(s) of interest. The brain is approximately 60% fat; it therefore lacks rigidity and poses structural preservation challenges during sample preparation. Histological sample preparation procedures are generally transferable to MALDI-MSI; however, there are various limitations. Optimal cutting temperature compound (OCT) is commonly used to embed and mount fixed tissue onto the chuck inside the cryostat during cryosectioning. However, OCT contains potential interferences that are detrimental to MALDI-MSI, while fixation is undesirable for the analysis of some analytes either due to extraction or chemical modification (i.e., polar metabolites). Therefore, a method for both fixed and fresh tissue compatible with MALDI-MSI and histology is desirable to increase the breadth of analyte(s), maintain the topographies of the brain, and provide rigidity to the fragile tissue while eliminating background interference. The method we introduce uses precast gelatin-based molds in which a whole mouse brain is embedded, flash frozen, and cryosectioned in preparation for mass spectrometry imaging (MSI).