The Candida albicans transcription factor Cas5 couples stress responses, drug resistance and cell cycle regulation

We thank Cowen lab members for helpful discussions. We also thank David Rogers (University of Tennessee) for sharing microarray analysis of the CAS5 homozygous mutant, and Li Ang (University of Macau) for assistance in optimizing the ChIP-Seq experiments. J.L.X. is supported by a Canadian Institutes of Health Research Doctoral award and M.D.L. is supported by a Sir Henry Wellcome Postdoctoral Fellowship (Wellcome Trust 096072). B.T.G. holds an Ontario Graduate Scholarship. C.B. and B.J.A. are supported by the Canadian Institutes of Health Research Foundation Grants (FDN-143264 and -143265). D.J.K. is supported by a National Institute of Allergy and Infectious Diseases grant (1R01AI098450) and J.D.L.C.D. is supported by the University of Rochester School of Dentistry and Medicine PREP program (R25 GM064133). A.S. is supported by the Creighton University and the Nebraska Department of Health and Human Services (LB506-2017-55). K.H.W. is supported by the Science and Technology Development Fund of Macau S.A.R. (FDCT; 085/2014/A2). L.E.C. is supported by the Canadian Institutes of Health Research Operating Grants (MOP-86452 and MOP-119520), the Natural Sciences and Engineering Council (NSERC) of Canada Discovery Grants (06261 and 462167), and an NSERC E.W.R. Steacie Memorial Fellowship (477598).Peer reviewedPublisher PD

Circulating miRNA panel for prediction of acute graft-versus-host disease in lymphoma patients undergoing matched unrelated hematopoietic stem cell transplantation

Acute graft-versus-host disease (aGVHD) results in significant morbidity and mortality after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Noninvasive diagnostic and prognostic tests for aGVHD are currently lacking, but would be beneficial in predicting aGVHD and improving the safety of allo-HSCT. Circulating microRNAs exhibit marked stability and may serve as biomarkers in several clinical settings. Here, we evaluated the use of circulating microRNAs as predictive biomarkers of aGVHD in lymphoma patients after allo-HSCT from matched unrelated donors (MUDs). After receiving informed consent, we prospectively collected plasma samples from 24 lymphoma patients before and after unmanipulated MUD allo-HSCT; microRNAs were then isolated. Fourteen patients developed aGVHD symptoms at a median of 48 days (range: 32–90) post-transplantation. Two patients developed intestinal GVHD, eight cutaneous GVHD, and four multiorgan GVHD. The microRNA expression profile was examined using quantitative real-time polymerase chain reaction (qRT-PCR). MicroRNAs 194 and 518f were significantly upregulated in aGVHD samples compared with samples taken from non-aGVHD patients. Remarkably, these upregulated microRNAs could be detected before the onset of aGVHD. Pathway prediction analysis indicated that these microRNAs may regulate critical pathways involved in aGVHD pathogenesis. Considering the noninvasive characteristics of plasma sampling and the feasibility of detecting miRNAs after allo-HSCT using real-time polymerase chain reaction, our results indicate that circulating microRNAs have the potential to enable an earlier aGVHD diagnosis and might assist in individualizing therapeutic strategies after MUD allo-HSCT. Nevertheless, standardization of blood sampling and analysis protocols is mandatory for the introduction of miRNA profiling into routine clinical use

Integrative Multi-omics Analysis to Characterize Human Brain Ischemia

Stroke is a major cause of death and disability. A better comprehension of stroke pathophysiology is fundamental to reduce its dramatic outcome. The use of high-throughput unbiased omics approaches and the integration of these data might deepen the knowledge of stroke at the molecular level, depicting the interaction between different molecular units. We aimed to identify protein and gene expression changes in the human brain after ischemia through an integrative approach to join the information of both omics analyses. The translational potential of our results was explored in a pilot study with blood samples from ischemic stroke patients. Proteomics and transcriptomics discovery studies were performed in human brain samples from six deceased stroke patients, comparing the infarct core with the corresponding contralateral brain region, unveiling 128 proteins and 2716 genes significantly dysregulated after stroke. Integrative bioinformatics analyses joining both datasets exposed canonical pathways altered in the ischemic area, highlighting the most influential molecules. Among the molecules with the highest fold-change, 28 genes and 9 proteins were selected to be validated in five independent human brain samples using orthogonal techniques. Our results were confirmed for NCDN, RAB3C, ST4A1, DNM1L, A1AG1, A1AT, JAM3, VTDB, ANXA1, ANXA2, and IL8. Finally, circulating levels of the validated proteins were explored in ischemic stroke patients. Fluctuations of A1AG1 and A1AT, both up-regulated in the ischemic brain, were detected in blood along the first week after onset. In summary, our results expand the knowledge of ischemic stroke pathology, revealing key molecules to be further explored as biomarkers and/or therapeutic targets

Validation of oligoarrays for quantitative exploration of the transcriptome

<p>Abstract</p> <p>Background</p> <p>Oligoarrays have become an accessible technique for exploring the transcriptome, but it is presently unclear how absolute transcript data from this technique compare to the data achieved with tag-based quantitative techniques, such as massively parallel signature sequencing (MPSS) and serial analysis of gene expression (SAGE). By use of the TransCount method we calculated absolute transcript concentrations from spotted oligoarray intensities, enabling direct comparisons with tag counts obtained with MPSS and SAGE. The tag counts were converted to number of transcripts per cell by assuming that the sum of all transcripts in a single cell was 5·10⁵. Our aim was to investigate whether the less resource demanding and more widespread oligoarray technique could provide data that were correlated to and had the same absolute scale as those obtained with MPSS and SAGE.</p> <p>Results</p> <p>A number of 1,777 unique transcripts were detected in common for the three technologies and served as the basis for our analyses. The correlations involving the oligoarray data were not weaker than, but, similar to the correlation between the MPSS and SAGE data, both when the entire concentration range was considered and at high concentrations. The data sets were more strongly correlated at high transcript concentrations than at low concentrations. On an absolute scale, the number of transcripts per cell and gene was generally higher based on oligoarrays than on MPSS and SAGE, and ranged from 1.6 to 9,705 for the 1,777 overlapping genes. The MPSS data were on same scale as the SAGE data, ranging from 0.5 to 3,180 (MPSS) and 9 to1,268 (SAGE) transcripts per cell and gene. The sum of all transcripts per cell for these genes was 3.8·10⁵(oligoarrays), 1.1·10⁵(MPSS) and 7.6·10⁴(SAGE), whereas the corresponding sum for all detected transcripts was 1.1·10⁶(oligoarrays), 2.8·10⁵(MPSS) and 3.8·10⁵(SAGE).</p> <p>Conclusion</p> <p>The oligoarrays and TransCount provide quantitative transcript concentrations that are correlated to MPSS and SAGE data, but, the absolute scale of the measurements differs across the technologies. The discrepancy questions whether the sum of all transcripts within a single cell might be higher than the number of 5·10⁵suggested in the literature and used to convert tag counts to transcripts per cell. If so, this may explain the apparent higher transcript detection efficiency of the oligoarrays, and has to be clarified before absolute transcript concentrations can be interchanged across the technologies. The ability to obtain transcript concentrations from oligoarrays opens up the possibility of efficient generation of universal transcript databases with low resource demands.</p

Integrative Multi-omics Analysis to Characterize Human Brain Ischemia

Stroke is a major cause of death and disability. A better comprehension of stroke pathophysiology is fundamental to reduce its dramatic outcome. The use of high-throughput unbiased omics approaches and the integration of these data might deepen the knowledge of stroke at the molecular level, depicting the interaction between different molecular units. We aimed to identify protein and gene expression changes in the human brain after ischemia through an integrative approach to join the information of both omics analyses. The translational potential of our results was explored in a pilot study with blood samples from ischemic stroke patients. Proteomics and transcriptomics discovery studies were performed in human brain samples from six deceased stroke patients, comparing the infarct core with the corresponding contralateral brain region, unveiling 128 proteins and 2716 genes significantly dysregulated after stroke. Integrative bioinformatics analyses joining both datasets exposed canonical pathways altered in the ischemic area, highlighting the most influential molecules. Among the molecules with the highest fold-change, 28 genes and 9 proteins were selected to be validated in five independent human brain samples using orthogonal techniques. Our results were confirmed for NCDN, RAB3C, ST4A1, DNM1L, A1AG1, A1AT, JAM3, VTDB, ANXA1, ANXA2, and IL8. Finally, circulating levels of the validated proteins were explored in ischemic stroke patients. Fluctuations of A1AG1 and A1AT, both up-regulated in the ischemic brain, were detected in blood along the first week after onset. In summary, our results expand the knowledge of ischemic stroke pathology, revealing key molecules to be further explored as biomarkers and/or therapeutic targets. Graphical abstract: [Figure not available: see fulltext.].This work has been funded by Instituto de Salud Carlos III (PI15/00354, PI18/00804), MINECO (MTM2015-64465-C2-1R) and GRBIO (2014-SGR-464) and co-financed by the European Regional Development Fund (FEDER). Neurovascular Research Laboratory takes part in the Spanish stroke research network INVICTUS + (RD16/0019/0021). L.R is supported by a pre-doctoral fellowship from the Instituto de Salud Carlos III (IFI17/00012).Peer reviewe

Evaluation of Molecular Methods for the Detection and Quantification of Pathogen-Derived Nucleic Acids in Sediment

The accurate detection of pathogens in environmental matrices, such as sediment, is critical in understanding pathogen fate and behavior in the environment. In this study, we assessed the usefulness of methods for the detection and quantification of Vibrio spp. and norovirus (NoV) nucleic acids in sediment. For bacteria, a commonly used direct method using hexadecyltrimethylammonium bromide (CTAB) and phenol-chloroform-isoamyl alcohol (PCI) extraction was optimized, whereas for NoV, direct and indirect (virus elution—concentration) methods were evaluated. For quantification, commercially available quantitative PCR (qPCR) and reverse transcription qPCR (RT-qPCR) kits were tested alongside a digital PCR (dPCR) approach. CTAB-based extraction combined with 16 h polyethylene glycol 6000 (PEG6000) precipitation was found to be suitable for the direct extraction of high abundance bacterial and viral nucleic acids. For the indirect extraction of viral RNA, beef extract-based elution followed by PEG6000 precipitation and extraction using the NucliSENS® MiniMag® Nucleic Acid Purification System and the PowerViral® Environmental RNA/DNA Isolation Kit and qRT-PCR resulted in 83–112 and 63–69% recoveries of NoV, respectively. dPCR resulted in lower viral recoveries (47 and 9%) and ~4 orders of magnitude lower Vibrio concentrations (3.6–4.6 log(10) gc/100 g sediment) than was observed using qPCR. The use of internal controls during viral quantification revealed that the RT step was more affected by inhibitors than the amplification. The methods described here are suitable for the enumeration of viral and/or bacterial pathogens in sediment, however the use of internal controls to assess efficiency is recommended

A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels

We seek to evaluate the detection performance of a rapid primary screening tool of Covid-19 solely based on the cough sound from 8,380 clinically validated samples with laboratory molecular-test (2,339 Covid-19 positive and 6,041 Covid-19 negative). Samples were clinically labelled according to the results and severity based on quantitative RT-PCR (qRT-PCR) analysis, cycle threshold and lymphocytes count from the patients. Our proposed generic method is a algorithm based on Empirical Mode Decomposition (EMD) with subsequent classification based on a tensor of audio features and deep artificial neural network classifier with convolutional layers called DeepCough'. Two different versions of DeepCough based on the number of tensor dimensions, i.e. DeepCough2D and DeepCough3D, have been investigated. These methods have been deployed in a multi-platform proof-of-concept Web App CoughDetect to administer this test anonymously. Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of 98.800.83%, sensitivity of 96.431.85%, and specificity of 96.201.74%, and 81.08%5.05% AUC for the recognition of three severity levels. Our proposed web tool and underpinning algorithm for the robust, fast, point-of-need identification of Covid-19 facilitates the rapid detection of the infection. We believe that it has the potential to significantly hamper the Covid-19 pandemic across the world

Serum microRNAs as biomarker for active and latent tuberculosis infection in immunocompetent and immunodeficient hosts

Background: Expression patterns of microRNAs in body fluids show potential to be used as noninvasive rapid and accurate biomarkers for various diseases.The study aimed to (i) identify patterns of microRNA signatures for diagnosis of tuberculosis (TB) and (ii) assess significance of a patient’s genetic background on signature composition and diagnostic performance. Patients and Methods: The study enrolled consented participants from Europe and Africa. Circulating miRNAs were measured and compared between patients belonging to the following categories; (i) active pulmonary tuberculosis (PTB), (ii) healthy individuals (H), (iii) active pulmonary TB co-infected with HIV (PTB/HIV), (iv) latent TB infection (LTBI) and (v) other pulmonary infection (OPI). As a first step, pooled sera of 10 participants from each category and region of enrolment were measured by TaqMan low-density arrays. Secondly, the identified significant miRNA signatures were applied to 56 individual sera aiming to discriminate between H and PTB patients. Next, the identified miRNA signatures were analysed for their diagnostic performances using multivariate logistic analysis, and Relevance Vector Machine (RVM). The diagnostic performance of both models was evaluated by a leave-one-out-cross-validation (LOOCV).Results: Significant miRNA signatures that discriminated patient categories were selected from the pooled samples. After validation of these in 56 individual participants (36 from the European cohort and 20 from the African population); a signature of 15 miRNAs was observed to be significantly differently expressed between categories, and able to differentiate healthy individuals and from individuals with PTB with a diagnostic accuracy of 82% (CI 70.2-90.0) in the RVM and 77% (CI 64.2-85.9) in the logistic classification model. The analysis based on genetic background identified a signature of 10 miRNAs that was specific for the European cohort with a diagnostic accuracy of 83% (CI 68.1-92.1) in RVM, and 81% (65.0-90.3) in the logistic model. Whereas a signature of 12 miRNAs was specific to the African cohort and the diagnostic accuracy increased up to 95% (CI 76.4-99.1) and 100% (83.9-100.0) in RVM and logistic model, respectively.Conclusion: This proof-of-concept study showed that miRNA levels were significantly higher in patient with TB than in those without TB. miRNAs are a promising diagnostic candidate for TB, therefore further prospective evaluation of this diagnostic seems warranted