A Systematic Review of Gene Expression Studies in Critically Ill Patients with Sepsis and Community-Acquired Pneumonia

(1) Background: Sepsis is present in nearly 90% of critically ill patients with community-acquired pneumonia (CAP). This systematic review updates the information on studies that have assessed gene expression profiles in critically ill septic patients with CAP. (2) Methods: We searched for studies that satisfied the following criteria: (a) expression profile in critically ill patients with sepsis due to CAP, (b) presence of a control group, and (c) adult patients. Over-representation analysis was performed with clusterProfiler using the Hallmark and Reactome collections. (3) Results: A total of 4312 differentially expressed genes (DEGs) and sRNAs were included in the enrichment analysis. In the Hallmark collection, genes regulated by nuclear factor kappa B in response to tumor necrosis factor, genes upregulated by signal transducer and activator of transcription 5 in response to interleukin 2 stimulation, genes upregulated in response to interferon-gamma, genes defining the inflammatory response, a subgroup of genes regulated by MYC-version 1 (v1), and genes upregulated during transplant rejection were significantly enriched in critically ill septic patients with CAP. Moreover, 88 pathways were identified in the Reactome database. (4) Conclusions: This study summarizes the reported DEGs in critically ill septic patients with CAP and investigates their functional implications. The results highlight the complexity of immune responses during CAP

Dynamics of Gene Expression Profiling and Identification of High-Risk Patients for Severe COVID-19

The clinical manifestations of SARS-CoV-2 infection vary widely, from asymptomatic infection to the development of acute respiratory distress syndrome (ARDS) and death. The host response elicited by SARS-CoV-2 plays a key role in determining the clinical outcome. We hypothesized that determining the dynamic whole blood transcriptomic profile of hospitalized adult COVID-19 patients and characterizing the subgroup that develops severe disease and ARDS would broaden our understanding of the heterogeneity in clinical outcomes. We recruited 60 hospitalized patients with RT-PCR-confirmed SARS-CoV-2 infection, among whom 19 developed ARDS. Peripheral blood was collected using PAXGene RNA tubes within 24 h of admission and on day 7. There were 2572 differently expressed genes in patients with ARDS at baseline and 1149 at day 7. We found a dysregulated inflammatory response in COVID-19 ARDS patients, with an increased expression of genes related to pro-inflammatory molecules and neutrophil and macrophage activation at admission, in addition to an immune regulation loss. This led, in turn, to a higher expression of genes related to reactive oxygen species, protein polyubiquitination, and metalloproteinases in the latter stages. Some of the most significant differences in gene expression found between patients with and without ARDS corresponded to long non-coding RNA involved in epigenetic control

MicroRNA expression profiling and DNA methylation signature for deregulated microRNA in cutaneous T-cell lymphoma

MicroRNAs usually regulate gene expression negatively, and aberrant expression has been involved in the development of several types of cancers. Microarray profiling of microRNA expression was performed to define a microRNA signature in a series of mycosis fungoides tumor stage (MFt, n=21) and CD30+ primary cutaneous anaplastic large cell lymphoma (CD30+ cALCL, n=11) samples in comparison with inflammatory dermatoses (ID, n=5). Supervised clustering confirmed a distinctive microRNA profile for cutaneous T-cell lymphoma (CTCL) with respect to ID. A 40 microRNA signature was found in MFt including upregulated onco-microRNAs (miR-146a, miR-142-3p/5p, miR-21, miR-181a/b, and miR-155) and downregulated tumor-suppressor microRNAs (miR-200ab/429 cluster, miR-10b, miR-193b, miR-141/200c, and miR-23b/27b). Regarding CD30+ cALCL, 39 differentially expressed microRNAs were identified. Particularly, overexpression of miR-155, miR-21, or miR-142-3p/5p and downregulation of the miR-141/200c clusters were observed. DNA methylation in microRNA gene promoters, as expression regulatory mechanism for deregulated microRNAs, was analyzed using Infinium 450K array and approximately one-third of the differentially expressed microRNAs showed significant DNA methylation differences. Two different microRNA methylation signatures for MFt and CD30+ cALCL were found. Correlation analysis showed an inverse relationship for microRNA promoter methylation and microRNA expression. These results reveal a subgroup-specific epigenetically regulated microRNA signatures for MFt and CD30+ cALCL patients

Reducing MYC's transcriptional footprint unveils a good prognostic gene signature in melanoma

MYC; Omomyc; MelanomaMYC; Omomyc; MelanomaMYC; Omomyc; MelanomaMYC's key role in oncogenesis and tumor progression has long been established for most human cancers. In melanoma, its deregulated activity by amplification of 8q24 chromosome or by upstream signaling coming from activating mutations in the RAS/RAF/MAPK pathway—the most predominantly mutated pathway in this disease—turns MYC into not only a driver but also a facilitator of melanoma progression, with documented effects leading to an aggressive clinical course and resistance to targeted therapy. Here, by making use of Omomyc, the most characterized MYC inhibitor to date that has just successfully completed a phase I clinical trial, we show for the first time that MYC inhibition in melanoma induces remarkable transcriptional modulation, resulting in severely compromised tumor growth and a clear abrogation of metastatic capacity independently of the driver mutation. By reducing MYC's transcriptional footprint in melanoma, Omomyc elicits gene expression profiles remarkably similar to those of patients with good prognosis, underlining the therapeutic potential that such an approach could eventually have in the clinic in this dismal disease.M.F.Z.-F. was supported by the Juan de la Cierva Programme of the Spanish Ministry of Economy and Competitiveness (IJCI-2014-22403) and Fundació La Marató de TV3 (grant 474/C/2019); F.G. was supported by Spanish Ministry of Science and Innovation Contratos Predoctorales de Formación en Investigación en Salud (PFIS; FI20/00274); I.G.-L. was supported by a grant from the University Teacher Training Program (FPU), Ministry of Universities (FPU20/04812); and S.M.-M. was supported by the Generalitat de Catalunya “Contractació de Personal Investigador Novell (FI-DGR)” 2016 fellowship (2016FI_B 00592). This project was funded by grants from the Spanish Ministry of Science and Innovation (Fondo de Inversión en Salud [FIS] PI19/01277, which also supported I.G.-L. and S.M.-M, and Retos-Colaboración 2019 RTC2019-007067-1), La Marató TV3, the Generalitat de Catalunya AGAUR 2017 grant SGR-3193, and the European Research Council (ERC-PoC II/3079/SYST-iMYC [813132]). We thank the rest of the Soucek laboratory for critical reading of the manuscript, and the personnel at Vall d'Hebron Research Institute (VHIR) High Technology Unit. We acknowledge Vall d'Hebron Institute of Oncology and the Cellex Foundation for providing research facilities and equipment

Search for Specific Biomarkers of IFNβ Bioactivity in Patients with Multiple Sclerosis

Myxovirus A (MxA), a protein encoded by the MX1 gene with antiviral activity, has proven to be a sensitive measure of IFNβ bioactivity in multiple sclerosis (MS). However, the use of MxA as a biomarker of IFNβ bioactivity has been criticized for the lack of evidence of its role on disease pathogenesis and the clinical response to IFNβ. Here, we aimed to identify specific biomarkers of IFNβ bioactivity in order to compare their gene expression induction by type I IFNs with the MxA, and to investigate their potential role in MS pathogenesis. Gene expression microarrays were performed in PBMC from MS patients who developed neutralizing antibodies (NAB) to IFNβ at 12 and/or 24 months of treatment and patients who remained NAB negative. Nine genes followed patterns in gene expression over time similar to the MX1, which was considered the gold standard gene, and were selected for further experiments: IFI6, IFI27, IFI44L, IFIT1, HERC5, LY6E, RSAD2, SIGLEC1, and USP18. In vitro experiments in PBMC from healthy controls revealed specific induction of selected biomarkers by IFNβ but not IFNγ, and several markers, in particular USP18 and HERC5, were shown to be significantly induced at lower IFNβ concentrations and more selective than the MX1 as biomarkers of IFNβ bioactivity. In addition, USP18 expression was deficient in MS patients compared with healthy controls (p = 0.0004). We propose specific biomarkers that may be considered in addition to the MxA to evaluate IFNβ bioactivity, and to further explore their implication in MS pathogenesis

New approaches in omics data modelling

The breakthrough in the technological field has allowed the extraction of large amounts of the so-called omics data. The analysis and Integration of this type of data by means of advanced statistical and bioinformatics methods will allow the improvement in the management of diseases. The diversity and complexity of omics data has encouraged the development of hundreds of new statistical methods to meet this objective. Therefore, having the appropriate methods to accommodate different data distributions and modelling complex data structures becomes essential. This thesis presents advances in three directions in this regard. First, the study of several methods to assess non-linear associations which is relevant when assessing the effect of environmental exposures (i.e exposome) on complex diseases. The study is accompanied by the development of the R package nlOmicAssoc. Second, the simplex distribution is proposed to analyse methylome data since this distribution properly fits beta values that are generated in this type of studies. The extension to generalized linear models with simplex response is also proposed. Lastly, an R package, HOmics, has been developed to incorporate a priori biological knowledge into association studies by using Bayesian hierarchical models. It also implements methods to model the dependence between omics data, enabling data integrationL’avenç en el camp tecnològic ens ha permès obtenir grans quantitats de les anomenades dades òmiques. L’anàlisi i integració d’aquesta mena de dades mitjançant mètodes estadístics i bioinformàtics avançats ha de permetre la millora en el maneig de les malalties. La diversitat i complexitat de les dades òmiques ha incentivat el desenvolupament de centenars de nous mètodes estadístics per a complir amb aquest objectiu. Per tant, és primordial disposar de mètodes que acomodin les distribucions adequades i modelin estructures de dades complexes. Davant d’això, aquesta tesi presenta avenços en tres direccions. En primer lloc, l’estudi de diferents mètodes per a analitzar associacions no lineals, molt rellevant en estudis d’associació entre exposicions mediambientals (i.e. exposoma) i malalties complexes. Aquesta anàlisi va acompanyada del desenvolupament del paquet de R nlOmicAssoc. En segon lloc, es proposa utilitzar la distribució simplex per analitzar dades metilòmiques, donat que aquesta distribució ajusta els valors beta generats en aquesta mena d’estudis. També es formula l’extensió a models lineals generalitzats amb resposta simplex. I per últim, el paquet de R HOmics, que incorpora coneixement biològic als estudis d’associació mitjançant models Bayesians jeràrquics. També implementa mètodes per modelar la dependència entre dades òmiques, permetent la integració de dade

New approaches in omics data modelling

The breakthrough in the technological field has allowed the extraction of large amounts of the so-called omics data. The analysis and Integration of this type of data by means of advanced statistical and bioinformatics methods will allow the improvement in the management of diseases. The diversity and complexity of omics data has encouraged the development of hundreds of new statistical methods to meet this objective. Therefore, having the appropriate methods to accommodate different data distributions and modelling complex data structures becomes essential. This thesis presents advances in three directions in this regard. First, the study of several methods to assess non-linear associations which is relevant when assessing the effect of environmental exposures (i.e exposome) on complex diseases. The study is accompanied by the development of the R package nlOmicAssoc. Second, the simplex distribution is proposed to analyse methylome data since this distribution properly fits beta values that are generated in this type of studies. The extension to generalized linear models with simplex response is also proposed. Lastly, an R package, HOmics, has been developed to incorporate a priori biological knowledge into association studies by using Bayesian hierarchical models. It also implements methods to model the dependence between omics data, enabling data integrationL’avenç en el camp tecnològic ens ha permès obtenir grans quantitats de les anomenades dades òmiques. L’anàlisi i integració d’aquesta mena de dades mitjançant mètodes estadístics i bioinformàtics avançats ha de permetre la millora en el maneig de les malalties. La diversitat i complexitat de les dades òmiques ha incentivat el desenvolupament de centenars de nous mètodes estadístics per a complir amb aquest objectiu. Per tant, és primordial disposar de mètodes que acomodin les distribucions adequades i modelin estructures de dades complexes. Davant d’això, aquesta tesi presenta avenços en tres direccions. En primer lloc, l’estudi de diferents mètodes per a analitzar associacions no lineals, molt rellevant en estudis d’associació entre exposicions mediambientals (i.e. exposoma) i malalties complexes. Aquesta anàlisi va acompanyada del desenvolupament del paquet de R nlOmicAssoc. En segon lloc, es proposa utilitzar la distribució simplex per analitzar dades metilòmiques, donat que aquesta distribució ajusta els valors beta generats en aquesta mena d’estudis. També es formula l’extensió a models lineals generalitzats amb resposta simplex. I per últim, el paquet de R HOmics, que incorpora coneixement biològic als estudis d’associació mitjançant models Bayesians jeràrquics. També implementa mètodes per modelar la dependència entre dades òmiques, permetent la integració de dade

Strong and Lightweight Stereolithographically 3D-Printed Polymer Nanocomposites with Low Friction and High Toughness

Strong and lightweight polymer nanocomposites with low friction, high toughness, and complex shapes were obtained for the first time through an affordable stereolithographic 3D printer, using low amounts of TiO2 nanoparticles. Tridimensional solid structures (i.e., tensile bars, compressive test specimens, gyroid-type structures, and dense lattices) were obtained. Herein, we found that the compressive stress, compressive strain, yield strength, and toughness corresponding to 3D-printed polymer nanocomposites were simultaneously increased—which is uncommon—using low amounts (0.4 wt.%) of TiO2 nanoparticles. Furthermore, we obtained lightweight cylindrical structures exhibiting high resistance to compression with a low friction coefficient (µ~0.2), and the printability of complex and hollow structures was demonstrated

DNA methylation and gene expression integration in cardiovascular disease

Background: The integration of different layers of omics information is an opportunity to tackle the complexity of cardiovascular diseases (CVD) and to identify new predictive biomarkers and potential therapeutic targets. Our aim was to integrate DNA methylation and gene expression data in an effort to identify biomarkers related to cardiovascular disease risk in a community-based population. We accessed data from the Framingham Offspring Study, a cohort study with data on DNA methylation (Infinium HumanMethylation450 BeadChip; Illumina) and gene expression (Human Exon 1.0 ST Array; Affymetrix). Using the MOFA2 R package, we integrated these data to identify biomarkers related to the risk of presenting a cardiovascular event. Results: Four independent latent factors (9, 19, 21-only in women-and 27), driven by DNA methylation, were associated with cardiovascular disease independently of classical risk factors and cell-type counts. In a sensitivity analysis, we also identified factor 21 as associated with CVD in women. Factors 9, 21 and 27 were also associated with coronary heart disease risk. Moreover, in a replication effort in an independent study three of the genes included in factor 27 were also present in a factor identified to be associated with myocardial infarction (CDC42BPB, MAN2A2 and RPTOR). Factor 9 was related to age and cell-type proportions; factor 19 was related to age and B cells count; factor 21 pointed to human immunodeficiency virus infection-related pathways and inflammation; and factor 27 was related to lifestyle factors such as alcohol consumption, smoking and body mass index. Inclusion of factor 21 (only in women) improved the discriminative and reclassification capacity of the Framingham classical risk function and factor 27 improved its discrimination. Conclusions: Unsupervised multi-omics data integration methods have the potential to provide insights into the pathogenesis of cardiovascular diseases. We identified four independent factors (one only in women) pointing to inflammation, endothelium homeostasis, visceral fat, cardiac remodeling and lifestyles as key players in the determination of cardiovascular risk. Moreover, two of these factors improved the predictive capacity of a classical risk function

Identification of differentially expressed genes in actinic keratosis samples treated with ingenol mebutate gel

Actinic keratosis is a common skin disease that may progress to invasive squamous cell carcinoma if left untreated. Ingenol mebutate has demonstrated efficacy in field treatment of actinic keratosis. However, molecular mechanisms on ingenol mebutate response are not yet fully understood. In this study, we evaluated the gene expression profiles of actinic keratosis lesions before and after treatment with ingenol mebutate using microarray technology. Actinic keratoses on face/scalp of 15 immunocompetent patients were identified and evaluated after treatment with topical ingenol mebutate gel 0.015%, applied once daily for 3 consecutive days. Diagnostic and clearance of lesions was determined by clinical, dermoscopic, and reflectance confocal microscopy criteria. Lesional and non-lesional skin biopsies were subjected to gene expression analysis profiled by Affymetrix microarray. Differentially expressed genes were identified, and enrichment analyses were performed using STRING database. At 8 weeks post-treatment, 60% of patients responded to ingenol mebutate therapy, achieving complete clearance in 40% of cases. A total of 128 differentially expressed genes were identified following treatment, and downregulated genes (114 of 128) revealed changes in pathways important to epidermal development, keratinocyte differentiation and cornification. In responder patients, 388 downregulated genes (of 450 differentially expressed genes) were also involved in development/differentiation of the epidermis, and immune system-related pathways, such as cytokine and interleukin signaling. Cluster analysis revealed two relevant clusters showing upregulated profile patterns in pre-treatment actinic keratoses of responders, as compared to non-responders. Again, differentially expressed genes were mainly associated with cornification, keratinization and keratinocyte differentiation. Overall, the present study provides insight into the gene expression profile of actinic keratoses after treatment with ingenol mebutate, as well as identification of genetic signatures that could predict treatment response