Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome

Pattern-centric transformation of omics data grounded on discriminative gene associations aids predictive tasks in TCGA while ensuring interpretability

The increasing prevalence of omics data sources is pushing the study of regulatory mechanisms underlying complex diseases such as cancer. However, the vast quantities of molecular features produced and the inherent interplay between them lead to a level of complexity that hampers both descriptive and predictive tasks, requiring custom-built algorithms that can extract relevant information from these sources of data. We propose a transformation that moves data centered on molecules (e.g., transcripts and proteins) to a new data space focused on putative regulatory modules given by statistically relevant co-expression patterns. To this end, the proposed transformation extracts patterns from the data through biclustering and uses them to create new variables with guarantees of interpretability and discriminative power. The transformation is shown to achieve dimensionality reductions of up to 99% and increase predictive performance of various classifiers across multiple omics layers. Results suggest that omics data transformations from gene-centric to pattern-centric data supports both prediction tasks and human interpretation, notably contributing to precision medicine applications.

Unravelling shared mechanisms: insights from recent ME/CFS research to illuminate long COVID pathologies

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic illness often triggered by an initiating acute event, mainly viral infections. The transition from acute to chronic disease remains unknown, but interest in this phenomenon has escalated since the COVID-19 pandemic and the post-COVID-19 illness, termed 'long COVID' (LC). Both ME/CFS and LC share many clinical similarities. Here, we present recent findings in ME/CFS research focussing on proposed disease pathologies shared with LC. Understanding these disease pathologies and how they influence each other is key to developing effective therapeutics and diagnostic tests. Given that ME/CFS typically has a longer disease duration compared with LC, with symptoms and pathologies evolving over time, ME/CFS may provide insights into the future progression of LC.

Myalgic encephalomyelitis/chronic fatigue syndrome from current evidence to new diagnostic perspectives through skeletal muscle and metabolic disturbances

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a demanding medical condition for patients and society. It has raised much more public awareness after the COVID-19 pandemic since ME/CFS and long-COVID patients share many clinical symptoms such as debilitating chronic fatigue. However, unlike long COVID, the etiopathology of ME/CFS remains a mystery despite several decades' research. This review moves from pathophysiology of ME/CFS through the compelling evidence and most interesting hypotheses. It focuses on the pathophysiology of skeletal muscle by proposing the hypothesis that skeletal muscle tissue offers novel opportunities for diagnosis and treatment of this syndrome and that new evidence can help resolve the long-standing debate on terminology.

Bulk RNA sequencing for analysis of post COVID-19 condition in adolescents and young adults

BACKGROUND

Post COVID-19 condition (PCC) is a complication of SARS-COV-2 infection and can lead to long-term disability.

METHODS

The present study was designed to analyse the gene expression patterns of PCC through bulk RNA sequencing of whole blood and to explore the potential molecular mechanisms of PCC. Whole blood was collected from 80 participants enrolled in a prospective cohort study following SARS-CoV-2 infected and non-infected individuals for 6 months after recruitment and was used for bulk RNA sequencing. Identification of differentially expressed genes (DEG), pathway enrichment and immune cell deconvolution was performed to explore potential biological pathways involved in PCC.

RESULTS

We have found 13 differentially expressed genes associated with PCC. Enriched pathways were related to interferon-signalling and anti-viral immune processes.

CONCLUSION

The PCC transcriptome is characterized by a modest overexpression of interferon-stimulated genes, pointing to a subtle ongoing inflammatory response.

Methodology for biomarker discovery with reproducibility in microbiome data using machine learning

BACKGROUND

In recent years, human microbiome studies have received increasing attention as this field is considered a potential source for clinical applications. With the advancements in omics technologies and AI, research focused on the discovery for potential biomarkers in the human microbiome using machine learning tools has produced positive outcomes. Despite the promising results, several issues can still be found in these studies such as datasets with small number of samples, inconsistent results, lack of uniform processing and methodologies, and other additional factors lead to lack of reproducibility in biomedical research. In this work, we propose a methodology that combines the DADA2 pipeline for 16s rRNA sequences processing and the Recursive Ensemble Feature Selection (REFS) in multiple datasets to increase reproducibility and obtain robust and reliable results in biomedical research.

RESULTS

Three experiments were performed analyzing microbiome data from patients/cases in Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder (ASD), and Type 2 Diabetes (T2D). In each experiment, we found a biomarker signature in one dataset and applied to 2 other as further validation. The effectiveness of the proposed methodology was compared with other feature selection methods such as K-Best with F-score and random selection as a base line. The Area Under the Curve (AUC) was employed as a measure of diagnostic accuracy and used as a metric for comparing the results of the proposed methodology with other feature selection methods. Additionally, we use the Matthews Correlation Coefficient (MCC) as a metric to evaluate the performance of the methodology as well as for comparison with other feature selection methods.

CONCLUSIONS

We developed a methodology for reproducible biomarker discovery for 16s rRNA microbiome sequence analysis, addressing the issues related with data dimensionality, inconsistent results and validation across independent datasets. The findings from the three experiments, across 9 different datasets, show that the proposed methodology achieved higher accuracy compared to other feature selection methods. This methodology is a first approach to increase reproducibility, to provide robust and reliable results.

Biomarkers for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): a systematic review

BACKGROUND

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a multifaceted condition that affects most body systems. There is currently no known diagnostic biomarker; instead, diagnosis is dependent on application of symptom-based case criteria following exclusion of any other potential medical conditions. While there are some studies that report potential biomarkers for ME/CFS, their efficacy has not been validated. The aim of this systematic review is to collate and appraise literature pertaining to a potential biomarker(s) which may effectively differentiate ME/CFS patients from healthy controls.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Cochrane review guidelines. PubMed, Embase and Scopus were systematically searched for articles containing "biomarker" and "ME/CFS" keywords in the abstract or title and if they included the following criteria: (1) were observational studies published between December 1994 and April 2022; (2) involved adult human participants; (3) full text is available in English (4) original research; (5) diagnosis of ME/CFS patients made according to the Fukuda criteria (1994), Canadian Consensus Criteria (2003), International Consensus Criteria (2011) or Institute of Medicine Criteria (2015); (6) study investigated potential biomarkers of ME/CFS compared to healthy controls. Quality and Bias were assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Case Control Studies.

RESULTS

A total of 101 publications were included in this systematic review. Potential biomarkers ranged from genetic/epigenetic (19.8%), immunological (29.7%), metabolomics/mitochondrial/microbiome (14.85%), endovascular/circulatory (17.82%), neurological (7.92%), ion channel (8.91%) and physical dysfunction biomarkers (8.91%). Most of the potential biomarkers reported were blood-based (79.2%). Use of lymphocytes as a model to investigate ME/CFS pathology was prominent among immune-based biomarkers. Most biomarkers had secondary (43.56%) or tertiary (54.47%) selectivity, which is the ability for the biomarker to identify a disease-causing agent, and a moderate (59.40%) to complex (39.60%) ease-of-detection, including the requirement of specialised equipment.

CONCLUSIONS

All potential ME/CFS biomarkers differed in efficiency, quality, and translatability as a diagnostic marker. Reproducibility of findings between the included publications were limited, however, several studies validated the involvement of immune dysfunction in the pathology of ME/CFS and the use of lymphocytes as a model to investigate the pathomechanism of illness. The heterogeneity shown across many of the included studies highlights the need for multidisciplinary research and uniform protocols in ME/CFS biomarker research.

Saliva microRNA signature to diagnose endometriosis: A cost-effectiveness evaluation of the Endotest®

OBJECTIVE

To evaluate a saliva diagnostic test (Endotest®) for endometriosis compared with the conventional algorithm.

DESIGN

A cost-effectiveness analysis with a decision-tree model based on literature data.

SETTING

France.

POPULATION

Women with chronic pelvic pain.

METHODS

Strategy I is the French algorithm, representing the comparator. For strategy II, all patients have an Endotest®. For strategy III, patients undergo ultrasonography to detect endometrioma and patients with no endometrioma detected have an Endotest®. For strategy IV, patients with no endometrioma detected on ultrasonography undergo pelvic magnetic resonance imaging (MRI) to detect endometrioma and/or deep endometriosis. An Endotest® is then performed for patients with a negative result on MRI.

MAIN OUTCOMES MEASURES

Costs and accuracy rates and incremental cost-effectiveness ratios (ICERs). Three analyses were performed with an Endotest® priced at €500, €750, and €1000. Probabilistic sensitivity analysis was conducted with Monte Carlo simulations.

RESULTS

With an Endotest® priced at €750, the cost per correctly diagnosed case was €1542, €990, €919 and €1000, respectively, for strategies I, II, III and IV. Strategy I was dominated by all other strategies. Strategies IV, III and II were, respectively, preferred for a willingness-to-pay threshold below €473, between €473 and €4670, and beyond €4670 per correctly diagnosed case. At a price of €500 per Endotest®, strategy I was dominated by all other strategies. At €1000, the ICERs of strategies II and III were €724 and €387 per correctly diagnosed case, respectively, compared with strategy I.

CONCLUSION

The present study demonstrates the value of the Endotest® from an economic perspective.

Endometriosis-associated infertility diagnosis based on saliva microRNA signatures

RESEARCH QUESTION

Can a saliva-based miRNA signature for endometriosis-associated infertility be designed and validated by analysing the human miRNome?

DESIGN

The prospective ENDOmiARN study (NCT04728152) included 200 saliva samples obtained between January 2021 and June 2021 from women with pelvic pain suggestive of endometriosis. All patients underwent either laparoscopy, magnetic resonance imaging, or both. Patients diagnosed with endometriosis were allocated to one of two groups according to their fertility status. Data analysis consisted of identifying a set of miRNA biomarkers using next-generation sequencing, and development of a saliva-based miRNA signature of infertility among patients with endometriosis based on a random forest model.

RESULTS

Among the 153 patients diagnosed with endometriosis, 24% (n = 36) were infertile and 76% (n = 117) were fertile. Small RNA-sequencing of the 153 saliva samples yielded approximately 3712 M raw sequencing reads (from ∼13.7 M to ∼39.3 M reads/sample). Of the 2561 known miRNAs, the feature selection method generated a signature of 34 miRNAs linked to endometriosis-associated infertility. After validation, the most accurate signature model had a sensitivity, specificity and area under the curve of 100%.

CONCLUSION

A saliva-based miRNA signature for endometriosis-associated infertility is reported. Although the results still require external validation before using the signature in routine practice, this non-invasive tool is likely to have a major effect on care provided to women with endometriosis.

A Bioinformatics Approach to MicroRNA-Sequencing Analysis Based on Human Saliva Samples of Patients with Endometriosis

Endometriosis, defined by the presence of endometrium-like tissue outside the uterus, affects 2–10% of the female population, i.e., around 190 million women, worldwide. The aim of the prospective ENDO-miRNA study was to develop a bioinformatics approach for microRNA-sequencing analysis of 200 saliva samples for miRNAome expression and to test its diagnostic accuracy for endometriosis. Among the 200 patients, 76.5% (n = 153) had confirmed endometriosis and 23.5% (n = 47) had no endometriosis (controls). Small RNA-seq of 200 saliva samples yielded ~4642 M raw sequencing reads (from ~13.7 M to ~39.3 M reads/sample). The number of expressed miRNAs ranged from 1250 (outlier) to 2561 per sample. Some 2561 miRNAs were found to be differentially expressed in the saliva samples of patients with endometriosis compared with the control patients. Among these, 1.17% (n = 30) were up- or downregulated. Among these, the F1-score, sensitivity, specificity, and AUC ranged from 11–86.8%, 5.8–97.4%, 10.6–100%, and 39.3–69.2%, respectively. Here, we report a bioinformatic approach to saliva miRNA sequencing and analysis. We underline the advantages of using saliva over blood in terms of ease of collection, reproducibility, stability, safety, non-invasiveness. This report describes the whole saliva transcriptome to make miRNA quantification a validated, standardized, and reliable technique for routine use. The methodology could be applied to build a saliva signature of endometriosis.

MicroRNome analysis generates a blood-based signature for endometriosis

Endometriosis, characterized by endometrial-like tissue outside the uterus, is thought to affect 2–10% of women of reproductive age: representing about 190 million women worldwide. Numerous studies have evaluated the diagnostic value of blood biomarkers but with disappointing results. Thus, the gold standard for diagnosing endometriosis remains laparoscopy. We performed a prospective trial, the ENDO-miRNA study, using both Artificial Intelligence (AI) and Machine Learning (ML), to analyze the current human miRNome to differentiate between patients with and without endometriosis, and to develop a blood-based microRNA (miRNA) diagnostic signature for endometriosis. Here, we present the first blood-based diagnostic signature obtained from a combination of two robust and disruptive technologies merging the intrinsic quality of miRNAs to condense the endometriosis phenotype (and its heterogeneity) with the modeling power of AI. The most accurate signature provides a sensitivity, specificity, and Area Under the Curve (AUC) of 96.8%, 100%, and 98.4%, respectively, and is sufficiently robust and reproducible to replace the gold standard of diagnostic surgery. Such a diagnostic approach for this debilitating disorder could impact recommendations from national and international learned societies.

Analysing the protection from respiratory tract infections and allergic diseases early in life by human milk components: the PRIMA birth cohort

Background

Many studies support the protective effect of breastfeeding on respiratory tract infections. Although infant formulas have been developed to provide adequate nutritional solutions, many components in human milk contributing to the protection of newborns and aiding immune development still need to be identified. In this paper we present the methodology of the “Protecting against Respiratory tract lnfections through human Milk Analysis” (PRIMA) cohort, which is an observational, prospective and multi-centre birth cohort aiming to identify novel functions of components in human milk that are protective against respiratory tract infections and allergic diseases early in life.

Methods

For the PRIMA human milk cohort we aim to recruit 1000 mother–child pairs in the first month postpartum. At one week, one, three, and six months after birth, fresh human milk samples will be collected and processed. In order to identify protective components, the level of pathogen specific antibodies, T cell composition, Human milk oligosaccharides, as well as extracellular vesicles (EVs) will be analysed, in the milk samples in relation to clinical data which are collected using two-weekly parental questionnaires. The primary outcome of this study is the number of parent-reported medically attended respiratory infections. Secondary outcomes that will be measured are physician diagnosed (respiratory) infections and allergies during the first year of life.

Discussion

The PRIMA human milk cohort will be a large prospective healthy birth cohort in which we will use an integrated, multidisciplinary approach to identify the longitudinal effect human milk components that play a role in preventing (respiratory) infections and allergies during the first year of life. Ultimately, we believe that this study will provide novel insights into immunomodulatory components in human milk. This may allow for optimizing formula feeding for all non-breastfed infants.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07107-w.

Salivary MicroRNA Signature for Diagnosis of Endometriosis

BACKGROUND

Endometriosis diagnosis constitutes a considerable economic burden for the healthcare system with diagnostic tools often inconclusive with insufficient accuracy. We sought to analyze the human miRNAome to define a saliva-based diagnostic miRNA signature for endometriosis.

METHODS

We performed a prospective ENDO-miRNA study involving 200 saliva samples obtained from 200 women with chronic pelvic pain suggestive of endometriosis collected between January and June 2021. The study consisted of two parts: (i) identification of a biomarker based on genome-wide miRNA expression profiling by small RNA sequencing using next-generation sequencing (NGS) and (ii) development of a saliva-based miRNA diagnostic signature according to expression and accuracy profiling using a Random Forest algorithm.

RESULTS

Among the 200 patients, 76.5% (n = 153) were diagnosed with endometriosis and 23.5% (n = 47) without (controls). Small RNA-seq of 200 saliva samples yielded ~4642 M raw sequencing reads (from ~13.7 M to ~39.3 M reads/sample). Quantification of the filtered reads and identification of known miRNAs yielded ~190 M sequences that were mapped to 2561 known miRNAs. Of the 2561 known miRNAs, the feature selection with Random Forest algorithm generated after internally cross validation a saliva signature of endometriosis composed of 109 miRNAs. The respective sensitivity, specificity, and AUC for the diagnostic miRNA signature were 96.7%, 100%, and 98.3%.

CONCLUSIONS

The ENDO-miRNA study is the first prospective study to report a saliva-based diagnostic miRNA signature for endometriosis. This could contribute to improving early diagnosis by means of a non-invasive tool easily available in any healthcare system.

Dietary Supplementation throughout Life with Non-Digestible Oligosaccharides and/or n-3 Poly-Unsaturated Fatty Acids in Healthy Mice Modulates the Gut-Immune System-Brain Axis

The composition and activity of the intestinal microbial community structures can be beneficially modulated by nutritional components such as non-digestible oligosaccharides and omega-3 poly-unsaturated fatty acids (n-3 PUFAs). These components affect immune function, brain development and behaviour. We investigated the additive effect of a dietary combination of scGOS:lcFOS and n-3 PUFAs on caecal content microbial community structures and development of the immune system, brain and behaviour from day of birth to early adulthood in healthy mice. Male BALB/cByJ mice received a control or enriched diet with a combination of scGOS:lcFOS (9:1) and 6% tuna oil (n-3 PUFAs) or individually scGOS:lcFOS (9:1) or 6% tuna oil (n-3 PUFAs). Behaviour, caecal content microbiota composition, short-chain fatty acid levels, brain monoamine levels, enterochromaffin cells and immune parameters in the mesenteric lymph nodes (MLN) and spleen were assessed. Caecal content microbial community structures displayed differences between the control and dietary groups, and between the dietary groups. Compared to control diet, the scGOS:lcFOS and combination diets increased caecal saccharolytic fermentation activity. The diets enhanced the number of enterochromaffin cells. The combination diet had no effects on the immune cells. Although the dietary effect on behaviour was limited, serotonin and serotonin metabolite levels in the amygdala were increased in the combination diet group. The combination and individual interventions affected caecal content microbial profiles, but had limited effects on behaviour and the immune system. No apparent additive effect was observed when scGOS:lcFOS and n-3 PUFAs were combined. The results suggest that scGOS:lcFOS and n-3 PUFAs together create a balance-the best of both in a healthy host.

ME/CFS: Past, Present and Future

This review raises a number of compelling issues related to the condition of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Some historical perspective is necessary in order to highlight the nature of the controversy concerning its causation. Throughout history, a pattern tends to repeat itself when natural phenomena require explanation. Dogma usually arrives first, then it is eventually replaced by scientific understanding. The same pattern is unfolding in relation to ME/CFS, but supporters of the psychological dogma surrounding its causation remain stubbornly resistant, even in the face of compelling scientific evidence to the contrary. Acceptance of the latter is not just an academic issue; the route to proper understanding and treatment of ME/CFS is through further scientific research rather than psychological theorisation. Only then will a long-suffering patient group benefit.