Universidad Católica de Valencia San Vicente Mártir

Post-COVID-19 condition, such as Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Fibromyalgia (FM), are characterized by fatigue, pain, shortness of breath, sleep disturbances, cognitive dysfunction and other symptoms, heavily impacting on patients daily functioning. Moreover, over half of patients end up fulfilling ME/CFS and/or FM clinical criteria after a few months of SARS-CoV-2 infection. Expression of the toxic human endogenous retrovirus (HERV)-W ENV protein can be induced by viral infection and HERV-W detection was correlated with acute COVID-19 severity and found significantly expressed in post-COVID-19 condition. This study shows that HERV-W ENV may also be present in prepandemic cases of ME/CFS, FM or co-diagnosed with both clinical criteria, suggesting viral participation in these chronic diseases. To learn whether associated antiviral mechanisms may also show differing patterns of immunological responses, we measured IgM, IgG, IgA and IgE antibody isotypes against SARS-CoV-2 spike and nucleocapsid antigens, the levels of IL-6, IL-8, IL-10, IFNγ and TNFα cytokines, the level of NfL, a neural damage biomarker, as well as some blood cell markers potentially related with fatigue. Importantly, some of the measured variables showed a capacity to discriminate post-COVID-19 condition cases from all other participants, with 100 % sensitivity and up to 71.9 % specificity providing a new tool for a differential diagnosis between diseases or syndromes with so many overlapping clinical symptoms. Interestingly, the detected markers showed moderate-to-strong correlations with patient symptoms pointing at novel therapeutic opportunities.

The inference of gene regulatory networks (GRNs) is a fundamental challenge in systems biology, aiming to decipher gene interactions from expression data. However, traditional inference techniques exhibit disparities in their results and a clear preference for specific datasets. To address this issue, we present BIO-INSIGHT (Biologically Informed Optimizer - INtegrating Software to Infer GRNs by Holistic Thinking), a parallel asynchronous many-objective evolutionary algorithm that optimizes the consensus among multiple inference methods guided by biologically relevant objectives. BIO-INSIGHT has been evaluated on an academic benchmark of 106 GRNs, comparing its performance against MO-GENECI and other consensus strategies. The results show a statistically significant improvement in AUROC and AUPR, demonstrating that biologically guided optimization outperforms primarily mathematical approaches. Additionally, BIO-INSIGHT was applied to gene expression data from patients with fibromyalgia, myalgic encephalomyelitis, and co-diagnosis of both diseases. The inferred networks revealed regulatory interactions specific to each condition, suggesting its clinical utility in biomarker identification and potential therapeutic targets. The robustness and ingenuity of BIO-INSIGHT consolidate its potential as an innovative tool for GRN inference, enabling the generation of more accurate and biologically feasible networks. The source code is hosted in a public GitHub repository under the MIT license: https://github.com/AdrianSeguraOrtiz/BIO-INSIGHT. Moreover, to facilitate its reproducibility and usage, the software associated with this implementation has been packaged into a Python library available on PyPI: https://pypi.org/project/GENECI/3.0.1/.