Metabolomic biomarkers of pancreatic cancer: a meta-analysis study

Pancreatic cancer (PC) is an aggressive disease with high mortality rates, however, there is no blood test for early detection and diagnosis of this disease. Several research groups have reported on metabolomics based clinical investigations to identify biomarkers of PC, however there is a lack of a centralized metabolite biomarker repository that can be used for meta-analysis and biomarker validation. Furthermore, since the incidence of PC is associated with metabolic syndrome and Type 2 diabetes mellitus (T2DM), there is a need to uncouple these common metabolic dysregulations that may otherwise diminish the clinical utility of metabolomic biosignatures. Here, we attempted to externally replicate proposed metabolite biomarkers of PC reported by several other groups in an independent group of PC subjects. Our study design included a T2DM cohort that was used as a non-cancer control and a separate cohort diagnosed with colorectal cancer (CRC), as a cancer disease control to eliminate possible generic biomarkers of cancer. We used targeted mass spectrometry for quantitation of literature-curated metabolite markers and identified a biomarker panel that discriminates between normal controls (NC) and PC patients with high accuracy. Further evaluation of our model with CRC, however, showed a drop in specificity for the PC biomarker panel. Taken together, our study underscores the need for a more robust study design for cancer biomarker studies so as to maximize the translational value and clinical implementation.This work was supported by ACS IRG-92-152-17 pilot award number AWD4470404 to KU and AKC. The authors would like to acknowledge the Metabolomics Shared Resource in Georgetown University (Washington DC, USA) partially supported by NIH/NCI/CCSG grant P30-CA05100

Plasma Sphingomyelins in Late-Onset Alzheimer's Disease.

BackgroundAltered plasma levels of sphingolipids, including sphingomyelins (SM), have been found in mouse models of Alzheimer's disease (AD) and in AD patient plasma samples.ObjectiveThis study assesses fourteen plasma SM species in a late-onset AD (LOAD) patient cohort (n = 138).MethodsSpecimens from control, preclinical, and symptomatic subjects were analyzed using targeted mass-spectrometry-based metabolomic methods.ResultsTotal plasma SM levels were not significantly affected by age or cognitive status. However, one metabolite that has been elevated in manifest AD in several recent studies, SM OHC14:1, was reduced significantly in pre-clinical AD and MCI relative to normal controls.ConclusionWe recommend additional comprehensive plasma lipidomics in experimental and clinical biospecimens related to LOAD that might advance the utility of plasma sphingomyelin levels in molecular phenotyping and interpretations of pathobiological mechanisms

Identification of Mycobacterium Species from BACTEC MGIT (TM) Positive Cultures with Oligo-FISH and PNA-FISH Methods

Rapid and accurate diagnosis of mycobacteria is very important in the prevention and effective treatment of tuberculosis which is still a serious public health problem. Fluorescence in situ hybridization (FISH) method using rRNA targeted probes allows for precise and accurate identification of mixed microorganisms from cultures and directly from clinical samples within a few hours without the need for culture methods. In this study it was aimed to compare the diagnostic performance of two different FISH methods (Oligo-FISH and PNA-FISH) with the conventional culture methods for the identification of Mycobacterium spp. grown in BACTEC MGIT (TM) (Mycobacteria Growth Indicator Tube) system. A total of 60 MGIT (BD, USA) positive, 52 MGIT negative samples and 10 different reference strains were included in the study. 16S rRNA targeted oligonucleotide probes (Myc657: Mycobacterium subdivision, Eub338: Positive control, NonEub: Negative control) were used for oligo-FISH, and 16S rRNA targeted peptide nucleotide probes (MTC: Mycobacterium tuberculosis complex, NTM: Non-tuberculosis Mycobacterium, BacUni: Positive control) for PNA-FISH. Ehrlich-Ziehl-Neelsen staining (ARB) and Lowenstein-Jensen (LJ) culture methods were performed as conventional methods as well as MGIT 960 culture system. Of MGIT positive 60 samples (44 sputum, 4 tissue, 4 urine, 3 bronchoalveolar lavage, 3 CSF, 1 abscess, 1 peritoneal fluid), 29 (48.3%) were found positive for ARB and 44 (73.3%) with LJ culture methods giving a total of 59 positive results. Fifty-eight (96.6%) of those isolates were identified as MTC, and one (1.7%) as NTM by conventional methods. By using Oligo-FISH, 95% (57/60) of the isolates were identified as Mycobacterium spp., while three samples (5%) yielded negative result. By using PNA-FISH, 54 (91.5%) isolates were identified as mycobacteria, of them 53 (90%) were typed as MTC and 1 (1.7%) as NTM. Five isolates that were found positive with Oligo-FISH, but negative with PNA-FISH, yielded positive result with PNA-FISH method performed with minor modifications. It was determined that both FISH methods are more rapid (approximately 2-2.5 hours) and practical than the conventional culture methods and also PNA-FISH was more practical than Oligo-FISH. The sensitivity, specificity, positive and negative predictive values of the probes used for Oligo-FISH, were 96.6%, 100%, 100% and 96.4%, respectively. Those values for the probes used for PNA-FISH, were 91.5%, 100%, 100% and 91.4%, respectively (p< 0.0001). The compatibility of the methods was calculated with kappa statistical analysis, assigning perfect concordances between Oligo- and PNA-FISH methods, as well as between conventional and both of the FISH methods (kappa: 0.964, 0.929, 0.964; p= 0.001). The coverage of oligonucleotide and PNA probes was also checked by using 16S rRNA gene sequence database retrieved from the SILVA 102. It was determined that the rates of coverage were 86.5% for Eub338, 41.7% for Myc657, 84.2% for BacUni, 76.3% for MTC (100% for only M.tuberculosis and M.bovis) and 25.8% for NTM probes. In conclusion, Oligo- and PNA-FISH methods seem to be successful for rapid and accurate identification of Mycobacterium spp. from MGIT positive cultures in routine mycobacteriology laboratories without the need for expensive methods

Candida bracarensis Detected among Isolates of Candida glabrata by Peptide Nucleic Acid Fluorescence In Situ Hybridization: Susceptibility Data and Documentation of Presumed Infection▿

Molecular taxonomic studies have revealed new Candida species among phenotypically delineated species, the best example being Candida dubliniensis. This study was designed to determine the occurrence of two new molecularly defined species, Candida bracarensis and Candida nivariensis, which are closely related to and identified as Candida glabrata by phenotypic assays. A total of 137 recent clinical isolates of C. glabrata identified by phenotypic characteristics was tested with C. bracarensis and C. nivariensis species-specific peptide nucleic acid fluorescence in situ hybridization probes. Three of 137 (2.2%) isolates were positive with the C. bracarensis probe, whereas the control strain, but none of the clinical isolates, was positive with the C. nivariensis probe. D1/D2 sequencing confirmed the identification of the three isolates as representing C. bracarensis. Clinically, one C. bracarensis isolate was recovered from a presumed infection, a polymicrobial pelvic abscess in a patient with perforated diverticulitis. The other two isolates were recovered from two adult oncology patients who were only colonized. C. bracarensis was white on CHROMagar Candida, had variable API-20C patterns that overlapped with C. nivariensis and some C. glabrata isolates, and had variable results with a rapid trehalose assay. Interestingly, an isolate from one of the colonized oncology patients was resistant to fluconazole, itraconazole, voriconazole, and posaconazole in vitro. In summary, C. bracarensis was detected among clinical isolates of C. glabrata, while C. nivariensis was not. One C. bracarensis isolate causing a presumed deep infection was recovered, and another isolate was azole resistant. Whether clinical laboratories should identify C. bracarensis will require more data

What success can teach us about failure: the plasma metabolome of older adults with superior memory and lessons for Alzheimer's disease.

As the world population ages, primary prevention of age-related cognitive decline and disability will become increasingly important. Prevention strategies are often developed from an understanding of disease pathobiology, but models of biological success may provide additional useful insights. Here, we studied 224 older adults, some with superior memory performance (n&nbsp;= 41), some with normal memory performance (n&nbsp;= 109), and some with mild cognitive impairment or Alzheimer's disease (AD; n&nbsp;= 74) to understand metabolomic differences which might inform future interventions to promote cognitive health. Plasma metabolomics revealed significant differential abundance of 12 metabolites in those with superior memory relative to controls (receiver operating characteristic area under the curve [AUC]&nbsp;= 0.89) and the inverse abundance pattern in the mild cognitive impairment, AD (AUC&nbsp;= 1.0) and even preclinical AD groups relative to controls (AUC&nbsp;= 0.97). The 12 metabolites are components of key metabolic pathways regulating oxidative stress, inflammation, and nitric oxide bioavailability. These findings from opposite ends of the cognitive continuum highlight the role of these pathways in superior memory abilities and whose failure may contribute to age-related memory impairment. These pathways may be targeted to promote successful cognitive aging

Systems healthcare: a holistic paradigm for tomorrow.

Systems healthcare is a holistic approach to health premised on systems biology and medicine. The approach integrates data from molecules, cells, organs, the individual, families, communities, and the natural and man-made environment. Both extrinsic and intrinsic influences constantly challenge the biological networks associated with wellness. Such influences may dysregulate networks and allow pathobiology to evolve, resulting in early clinical presentation that requires astute assessment and timely intervention for successful mitigation. Herein, we describe the components of relevant biological systems and the nature of progression from at-risk to manifest disease. We illustrate the systems approach by examining two relevant clinical examples: Alzheimer's and cardiovascular diseases. The implications of systems healthcare management are examined through the lens of economics, ethics, policy and the law. Finally, we propose the need to develop new educational paradigms to enhance the training of the health professional in an era of systems medicine

A retrotransposon storm marks clinical phenoconversion to late-onset Alzheimer's disease

Recent reports have suggested that the reactivation of otherwise transcriptionally silent transposable elements (TEs) might induce brain degeneration, either by dysregulating the expression of genes and pathways implicated in cognitive decline and dementia or through the induction of immune-mediated neuroinflammation resulting in the elimination of neural and glial cells. In the work we present here, we test the hypothesis that differentially expressed TEs in blood could be used as biomarkers of cognitive decline and development of AD. To this aim, we used a sample of aging subjects (age &gt; 70) that developed late-onset Alzheimer's disease (LOAD) over a relatively short period of time (12-48&nbsp;months), for which blood was available before and after their phenoconversion, and a group of cognitive stable subjects as controls. We applied our developed and validated customized pipeline that allows the identification, characterization, and quantification of the differentially expressed (DE) TEs before and after the onset of manifest LOAD, through analyses of RNA-Seq data. We compared the level of DE TEs within more than 600,000 TE-mapping RNA transcripts from 25 individuals, whose specimens we obtained before and after their phenotypic conversion (phenoconversion) to LOAD, and discovered that 1790 TE transcripts showed significant expression differences between these two timepoints (logFC ± 1.5, logCMP &gt; 5.3, nominal p value &lt; 0.01). These DE transcripts mapped both over- and under-expressed TE elements. Occurring before the clinical phenoconversion, this TE storm features significant increases in DE transcripts of LINEs, LTRs, and SVAs, while those for SINEs are significantly depleted. These dysregulations end with signs of manifest LOAD. This set of highly DE transcripts generates a TE transcriptional profile that accurately discriminates the before and after phenoconversion states of these subjects. Our findings suggest that a storm of DE TEs occurs before phenoconversion from normal cognition to manifest LOAD in risk individuals compared to controls, and may provide useful blood-based biomarkers for heralding such a clinical transition, also suggesting that TEs can indeed participate in the complex process of neurodegeneration

Data from: Plasma metabolomic biomarkers accurately classify acute mild traumatic brain injury from controls

Past and recent attempts at devising objective biomarkers for traumatic brain injury (TBI) in both blood and cerebrospinal fluid have focused on abundance measures of time-dependent proteins. Similar independent determinants would be most welcome in diagnosing the most common form of TBI, mild TBI (mTBI), which remains difficult to define and confirm based solely on clinical criteria. There are currently no consensus diagnostic measures that objectively define individuals as having sustained an acute mTBI. Plasma metabolomic analyses have recently evolved to offer an alternative to proteomic analyses, offering an orthogonal diagnostic measure to what is currently available. The purpose of this study was to determine whether a developed set of metabolomic biomarkers is able to objectively classify college athletes sustaining mTBI from non-injured teammates, within 6 hours of trauma and whether such a biomarker panel could be effectively applied to an independent cohort of TBI and control subjects. A 6-metabolite panel was developed from biomarkers that had their identities confirmed using tandem mass spectrometry (MS/MS) in our Athlete cohort. These biomarkers were defined at ≤6 hours following mTBI and objectively classified mTBI athletes from teammate controls, and provided similar classification of these groups at the 2, 3, and 7 days post-mTBI. The same 6-metabolite panel, when applied to a separate, independent cohort provided statistically similar results despite major differences between the two cohorts. Our confirmed plasma biomarker panel objectively classifies acute mTBI cases from controls within 6 hours of injury in our two independent cohorts. While encouraged by our initial results, we expect future studies to expand on these initial observations