Tag: high-throughput analysis

Persistent complement dysregulation with signs of thromboinflammation in active Long Covid

Abstract:

Long Covid is a debilitating condition of unknown etiology. We performed multimodal proteomics analyses of blood serum from COVID-19 patients followed up to 12 months after confirmed severe acute respiratory syndrome coronavirus 2 infection. Analysis of >6500 proteins in 268 longitudinal samples revealed dysregulated activation of the complement system, an innate immune protection and homeostasis mechanism, in individuals experiencing Long Covid.

Thus, active Long Covid was characterized by terminal complement system dysregulation and ongoing activation of the alternative and classical complement pathways, the latter associated with increased antibody titers against several herpesviruses possibly stimulating this pathway. Moreover, markers of hemolysis, tissue injury, platelet activation, and monocyte–platelet aggregates were increased in Long Covid. Machine learning confirmed complement and thromboinflammatory proteins as top biomarkers, warranting diagnostic and therapeutic interrogation of these systems.

Source: Carlo Cervia-Hasler et al. Persistent complement dysregulation with signs of thromboinflammation in active Long Covid. Science383,eadg7942(2024). DOI: 10.1126/science.adg7942 https://www.science.org/doi/10.1126/science.adg7942 (Full text)

Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia Using a Portable FT-MIR Spectroscopic Combined with Chemometrics

Abstract:

Post Acute Sequelae of SARS-CoV-2 infection (PASC or Long COVID) is characterized by lingering symptomatology post-initial COVID-19 illness that is often debilitating. It is seen in up to 30–40% of individuals post-infection. Patients with Long COVID (LC) suffer from dysautonomia, malaise, fatigue, and pain, amongst a multitude of other symptoms.
Fibromyalgia (FM) is a chronic musculoskeletal pain disorder that often leads to functional disability and severe impairment of quality of life. LC and FM share several clinical features, including pain that often makes them indistinguishable. The aim of this study is to develop a metabolic fingerprinting approach using portable Fourier-transform mid-infrared (FT-MIR) spectroscopic techniques to diagnose clinically similar LC and FM.
Blood samples were obtained from LC (n = 50) and FM (n = 50) patients and stored on conventional bloodspot protein saver cards. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. Through the deconvolution analysis of the spectral data, a distinct spectral marker at 1565 cm−1 was identified based on a statistically significant analysis, only present in FM patients. This IR band has been linked to the presence of side chains of glutamate.
An OPLS-DA algorithm created using the spectral region 1500 to 1700 cm−1 enabled the classification of the spectra into their corresponding classes (Rcv > 0.96) with 100% accuracy and specificity. This high-throughput approach allows unique metabolic signatures associated with LC and FM to be identified, allowing these conditions to be distinguished and implemented for in-clinic diagnostics, which is crucial to guide future therapeutic approaches.
Source: Hackshaw KV, Yao S, Bao H, de Lamo Castellvi S, Aziz R, Nuguri SM, Yu L, Osuna-Diaz MM, Brode WM, Sebastian KR, et al. Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia Using a Portable FT-MIR Spectroscopic Combined with Chemometrics. Biomedicines. 2023; 11(10):2704. https://doi.org/10.3390/biomedicines11102704 https://www.mdpi.com/2227-9059/11/10/2704 (Full text)

A multicenter virome analysis of blood, feces, and saliva in myalgic encephalomyelitis/chronic fatigue syndrome

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is estimated to affect 0.4%-2.5% of the global population. Most cases are unexplained; however, some patients describe an antecedent viral infection or response to antiviral medications.

We report here a multicenter study for the presence of viral nucleic acid in blood, feces, and saliva of patients with ME/CFS using polymerase chain reaction and high-throughput sequencing.

We found no consistent group-specific differences other than a lower prevalence of anelloviruses in cases compared to healthy controls. Our findings suggest that future investigations into viral infections in ME/CFS should focus on adaptive immune responses rather than surveillance for viral gene products.

Source: Briese T, Tokarz R, Bateman L, Che X, Guo C, Jain K, Kapoor V, Levine S, Hornig M, Oleynik A, Quan PL, Wong WH, Williams BL, Vernon SD, Klimas NG, Peterson DL, Montoya JG, Ian Lipkin W. A multicenter virome analysis of blood, feces, and saliva in myalgic encephalomyelitis/chronic fatigue syndrome. J Med Virol. 2023 Aug;95(8):e28993. doi: 10.1002/jmv.28993. PMID: 37526404. https://pubmed.ncbi.nlm.nih.gov/37526404/ 

Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative

Abstract:

Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively.

With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients.

We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations.

Source: Zang C, Zhang Y, Xu J, Bian J, Morozyuk D, Schenck EJ, Khullar D, Nordvig AS, Shenkman EA, Rothman RL, Block JP, Lyman K, Weiner MG, Carton TW, Wang F, Kaushal R. Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative. Nat Commun. 2023 Apr 7;14(1):1948. doi: 10.1038/s41467-023-37653-z. PMID: 37029117; PMCID: PMC10080528. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10080528/ (Full text)

High-throughput sequencing of plasma microRNA in chronic fatigue syndrome/myalgic encephalomyelitis

Abstract:

BACKGROUND: MicroRNAs (miRNAs) are known to regulate many biological processes and their dysregulation has been associated with a variety of diseases including Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME). The recent discovery of stable and reproducible miRNA in plasma has raised the possibility that circulating miRNAs may serve as novel diagnostic markers. The objective of this study was to determine the role of plasma miRNA in CFS/ME.

RESULTS: Using Illumina high-throughput sequencing we identified 19 miRNAs that were differentially expressed in the plasma of CFS/ME patients in comparison to non-fatigued controls. Following RT-qPCR analysis, we were able to confirm the significant up-regulation of three miRNAs (hsa-miR-127-3p, hsa-miR-142-5p and hsa-miR-143-3p) in the CFS/ME patients.

CONCLUSION: Our study is the first to identify circulating miRNAs from CFS/ME patients and also to confirm three differentially expressed circulating miRNAs in CFS/ME patients, providing a basis for further study to find useful CFS/ME biomarkers.

 

Source: Brenu EW, Ashton KJ, Batovska J, Staines DR, Marshall-Gradisnik SM. High-throughput sequencing of plasma microRNA in chronic fatigue syndrome/myalgic encephalomyelitis. PLoS One. 2014 Sep 19;9(9):e102783. doi: 10.1371/journal.pone.0102783. ECollection 2014. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4169517/ (Full article)

 

High-throughput 16S rRNA gene sequencing reveals alterations of intestinal microbiota in myalgic encephalomyelitis/chronic fatigue syndrome patients

Abstract:

Human intestinal microbiota plays an important role in the maintenance of host health by providing energy, nutrients, and immunological protection. Intestinal dysfunction is a frequent complaint in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) patients, and previous reports suggest that dysbiosis, i.e. the overgrowth of abnormal populations of bacteria in the gut, is linked to the pathogenesis of the disease.

We used high-throughput 16S rRNA gene sequencing to investigate the presence of specific alterations in the gut microbiota of ME/CFS patients from Belgium and Norway. 43 ME/CFS patients and 36 healthy controls were included in the study. Bacterial DNA was extracted from stool samples, PCR amplification was performed on 16S rRNA gene regions, and PCR amplicons were sequenced using Roche FLX 454 sequencer.

The composition of the gut microbiota was found to differ between Belgian controls and Norwegian controls: Norwegians showed higher percentages of specific Firmicutes populations (Roseburia, Holdemania) and lower proportions of most Bacteroidetes genera. A highly significant separation could be achieved between Norwegian controls and Norwegian patients: patients presented increased proportions of Lactonifactor and Alistipes, as well as a decrease in several Firmicutes populations.

In Belgian subjects the patient/control separation was less pronounced, however some abnormalities observed in Norwegian patients were also found in Belgian patients. These results show that intestinal microbiota is altered in ME/CFS. High-throughput sequencing is a useful tool to diagnose dysbiosis in patients and could help designing treatments based on gut microbiota modulation (antibiotics, pre and probiotics supplementation).

Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

 

Source: Frémont M, Coomans D, Massart S, De Meirleir K. High-throughput 16S rRNA gene sequencing reveals alterations of intestinal microbiota in myalgic encephalomyelitis/chronic fatigue syndrome patients. Anaerobe. 2013 Aug;22:50-6. doi: 10.1016/j.anaerobe.2013.06.002. Epub 2013 Jun 19. https://www.ncbi.nlm.nih.gov/pubmed/23791918

 

An integrated approach to infer causal associations among gene expression, genotype variation, and disease

Abstract:

Gene expression data and genotype variation data are now capable of providing genome-wide patterns across many different clinical conditions. However, the separate analysis of these data has limitations in elucidating the complex network of gene interactions underlying complex traits, such as common human diseases. More information about the identity of key driver genes of common diseases comes from integrating these two heterogeneous types of data. We developed a two-step procedure to characterize complex diseases by integrating genotype variation data and gene expression data.

The first step elucidates the causal relationship among genetic variation, gene expression level, and disease. Based on the causal relationship determined at the first step, the second step identifies significant gene expression traits whose effects on disease status or whose responses to disease status are modified by the specific genotype variation. For the selected significant genes, a pathway enrichment analysis can be performed to identify the genetic mechanism of a complex disease. The proposed two-step procedure was shown to be an effective method for integrating three different levels of data, i.e., genotype variation, gene expression and disease status.

By applying the proposed procedure to a chronic fatigue syndrome (CFS) dataset, we identified a list of potential causal genes for CFS, and found an evidence for difference in genetic mechanisms of the etiology between CFS without ‘a major depressive disorder with melancholic features’ (CFS) and CFS with ‘a major depressive disorder with melancholic features’ (CFS-MDD/m). Especially, the SNPs within NR3C1 gene were shown to differently influence the susceptibility of developing CFS and CFS-MDD/m through integrative action with gene expression levels.

 

Source: Lee E, Cho S, Kim K, Park T. An integrated approach to infer causal associations among gene expression, genotype variation, and disease. Genomics. 2009 Oct;94(4):269-77. doi: 10.1016/j.ygeno.2009.06.002. Epub 2009 Jun 18. http://www.sciencedirect.com/science/article/pii/S0888754309001347 (Full article)

 

Bayesian biomarker identification based on marker-expression proteomics data

Abstract:

We are studying variable selection in multiple regression models in which molecular markers and/or gene-expression measurements as well as intensity measurements from protein spectra serve as predictors for the outcome variable (i.e., trait or disease state).

Finding genetic biomarkers and searching genetic-epidemiological factors can be formulated as a statistical problem of variable selection, in which, from a large set of candidates, a small number of trait-associated predictors are identified. We illustrate our approach by analyzing the data available for chronic fatigue syndrome (CFS).

CFS is a complex disease from several aspects, e.g., it is difficult to diagnose and difficult to quantify. To identify biomarkers we used microarray data and SELDI-TOF-based proteomics data. We also analyzed genetic marker information for a large number of SNPs for an overlapping set of individuals. The objectives of the analyses were to identify markers specific to fatigue that are also possibly exclusive to CFS. The use of such models can be motivated, for example, by the search for new biomarkers for the diagnosis and prognosis of cancer and measures of response to therapy. Generally, for this we use Bayesian hierarchical modeling and Markov Chain Monte Carlo computation.

 

Source: Bhattacharjee M, Botting CH, Sillanpää MJ. Bayesian biomarker identification based on marker-expression proteomics data. Genomics. 2008 Dec;92(6):384-92. doi: 10.1016/j.ygeno.2008.06.006. Epub 2008 Aug 15. http://www.sciencedirect.com/science/article/pii/S0888754308001420 (Full article)