Tag: network analysis

A Network Medicine Approach to Investigating ME/CFS Pathogenesis in Severely Ill Patients: A Pilot Study

Abstract:

This pilot study harnessed the power of network medicine to unravel the complex pathogenesis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). By utilizing a network analysis on whole genome sequencing (WGS) data from the Severely Ill Patient Study (SIPS), we identified ME/CFS-associated proteins and delineated the corresponding network-level module, termed the SIPS disease module, together with its relevant pathways. This module demonstrated significant overlap with genes implicated in fatigue, cognitive disorders, and neurodegenerative diseases.

Our pathway analysis revealed potential associations between ME/CFS and conditions such as COVID-19, Epstein-Barr virus (EBV) infection, neurodegenerative diseases, and pathways involved in cortisol synthesis and secretion, supporting the hypothesis that ME/CFS is a neuroimmune disorder. Additionally, our findings underscore a potential link between ME/CFS and estrogen signaling pathways, which may elucidate the higher prevalence of ME/CFS in females.

These findings provide insights into the pathogenesis of ME/CFS from a network medicine perspective and highlight potential therapeutic targets. Further research is needed to validate these findings and explore their implications for improving diagnosis and treatment.

Source: Li-Yuan Hung, Chan-Shuo Wu, Chia-Jung Chang, Peng Li, Kimberly Hicks, Becky Taurog, Joshua J Dibble, Braxton Morrison, Chimere L Smith, Ronald W Davis, Wenzhong Xiao. A Network Medicine Approach to Investigating ME/CFS Pathogenesis in Severely Ill Patients: A Pilot Study.
medRxiv 2024.09.26.24314417; doi: https://doi.org/10.1101/2024.09.26.24314417 https://www.medrxiv.org/content/10.1101/2024.09.26.24314417v1 (Full text available as PDF file)

Sequential multi-omics analysis identifies clinical phenotypes and predictive biomarkers for long COVID

Abstract:

The post-acute sequelae of COVID-19 (PASC), also known as long COVID, is often associated with debilitating symptoms and adverse multisystem consequences. We obtain plasma samples from 117 individuals during and 6 months following their acute phase of infection to comprehensively profile and assess changes in cytokines, proteome, and metabolome.

Network analysis reveals sustained inflammatory response, platelet degranulation, and cellular activation during convalescence accompanied by dysregulation in arginine biosynthesis, methionine metabolism, taurine metabolism, and tricarboxylic acid (TCA) cycle processes.

Furthermore, we develop a prognostic model composed of 20 molecules involved in regulating T cell exhaustion and energy metabolism that can reliably predict adverse clinical outcomes following discharge from acute infection with 83% accuracy and an area under the curve (AUC) of 0.96.

Our study reveals pertinent biological processes during convalescence that differ from acute infection, and it supports the development of specific therapies and biomarkers for patients suffering from long COVID.

Source: Wang K, Khoramjoo M, Srinivasan K, Gordon PMK, Mandal R, Jackson D, Sligl W, Grant MB, Penninger JM, Borchers CH, Wishart DS, Prasad V, Oudit GY. Sequential multi-omics analysis identifies clinical phenotypes and predictive biomarkers for long COVID. Cell Rep Med. 2023 Oct 18:101254. doi: 10.1016/j.xcrm.2023.101254. Epub ahead of print. PMID: 37890487. https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(23)00431-7 (Full text)

Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing

Background: Since the beginning of the COVID-19 pandemic, over 480 million people have been infected and more than 6 million people have died from COVID-19 worldwide. In some patients with acute COVID-19, symptoms manifest over a longer period, which is also called “long-COVID.” Unmet medical needs related to long-COVID are high, since there are no treatments approved. Patients experiment with various medications and supplements hoping to alleviate their suffering. They often share their experiences on social media.

Objective: The aim of this study was to explore the feasibility of social media mining methods to extract important compounds from the perspective of patients. The goal is to provide an overview of different medication strategies and important agents mentioned in Reddit users’ self-reports to support hypothesis generation for drug repurposing, by incorporating patients’ experiences.

Methods:We used named-entity recognition to extract substances representing medications or supplements used to treat long-COVID from almost 70,000 posts on the “/r/covidlonghaulers” subreddit. We analyzed substances by frequency, co-occurrences, and network analysis to identify important substances and substance clusters.

Results: The named-entity recognition algorithm achieved an F1 score of 0.67. A total of 28,447 substance entities and 5789 word co-occurrence pairs were extracted. “Histamine antagonists,” “famotidine,” “magnesium,” “vitamins,” and “steroids” were the most frequently mentioned substances. Network analysis revealed three clusters of substances, indicating certain medication patterns.

Conclusions: This feasibility study indicates that network analysis can be used to characterize the medication strategies discussed in social media. Comparison with existing literature shows that this approach identifies substances that are promising candidates for drug repurposing, such as antihistamines, steroids, or antidepressants. In the context of a pandemic, the proposed method could be used to support drug repurposing hypothesis development by prioritizing substances that are important to users.

Source: Koss J, Bohnet-Joschko S. Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing. JMIR Form Res. 2022 Oct 3;6(10):e39582. doi: 10.2196/39582. PMID: 36007131; PMCID: PMC9531770. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531770/ (Full text)

Network autonomic analysis of post-acute sequelae of COVID-19 and postural tachycardia syndrome

Abstract:

Background: The autonomic nervous system (ANS) is a complex network where sympathetic and parasympathetic domains interact inside and outside of the network. Correlation-based network analysis (NA) is a novel approach enabling the quantification of these interactions. The aim of this study is to assess the applicability of NA to assess relationships between autonomic, sensory, respiratory, cerebrovascular, and inflammatory markers on post-acute sequela of COVID-19 (PASC) and postural tachycardia syndrome (POTS).

Methods: In this retrospective study, datasets from PASC (n = 15), POTS (n = 15), and matched controls (n = 11) were analyzed. Networks were constructed from surveys (autonomic and sensory), autonomic tests (deep breathing, Valsalva maneuver, tilt, and sudomotor test) results using heart rate, blood pressure, cerebral blood flow velocity (CBFv), capnography, skin biopsies for assessment of small fiber neuropathy (SFN), and various inflammatory markers. Networks were characterized by clusters and centrality metrics.

Results: Standard analysis showed widespread abnormalities including reduced orthostatic CBFv in 100%/88% (PASC/POTS), SFN 77%/88%, mild-to-moderate dysautonomia 100%/100%, hypocapnia 87%/100%, and elevated inflammatory markers. NA showed different signatures for both disorders with centrality metrics of vascular and inflammatory variables playing prominent roles in differentiating PASC from POTS.

Conclusions: NA is suitable for a relationship analysis between autonomic and nonautonomic components. Our preliminary analyses indicate that NA can expand the value of autonomic testing and provide new insight into the functioning of the ANS and related systems in complex disease processes such as PASC and POTS.

Source: Novak P, Giannetti MP, Weller E, Hamilton MJ, Mukerji SS, Alabsi HS, Systrom D, Marciano SP, Felsenstein D, Mullally WJ, Pilgrim DM, Castells M. Network autonomic analysis of post-acute sequelae of COVID-19 and postural tachycardia syndrome. Neurol Sci. 2022 Sep 28:1–12. doi: 10.1007/s10072-022-06423-y. Epub ahead of print. PMID: 36169757; PMCID: PMC9517969. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9517969/ (Full text)

Network Analysis of Symptoms Co-Occurrence in Chronic Fatigue Syndrome

Abstract:

Chronic fatigue syndrome (CFS) is a heterogenous disorder of multiple disabling symptoms with complex manifestations. Network analysis is a statistical and interrogative methodology to investigate the prevalence of symptoms (nodes) and their inter-dependent (inter-nodal) relationships. In the present study, we explored the co-occurrence of symptoms in a cohort of Polish CFS patients using network analysis.

A total of 110 patients with CFS were examined (75 females). The mean age of the total sample was 37.93 (8.5) years old while the mean duration of symptoms in years was 4.4 (4). Post-exertional malaise (PEM) was present in 75.45% of patients, unrefreshing sleep was noted in 89.09% and impaired memory or concentration was observed in 87.27% of patients. The least prevalent symptom was tender cervical or axillary lymph nodes, noted in 34.55% of the total sample.

Three of the most densely connected nodes were the total number of symptoms, sore throat and PEM. PEM was positively related with impairment in memory or concentration. Both PEM and impairment in memory or concentration presence are related to more severe fatigue measured by CFQ and FIS. PEM presence was positively related with the presence of multi-joint pain and negatively with tender lymph nodes and muscle pain. Sore throat was related with objective and subjective autonomic nervous system impairment. This study helps define symptom presentation of CFS with the pathophysiology of specific systems and links with multidisciplinary contemporary molecular pathology, including comparative MRI.

Source: Kujawski S, Słomko J, Newton JL, Eaton-Fitch N, Staines DR, Marshall-Gradisnik S, Zalewski P. Network Analysis of Symptoms Co-Occurrence in Chronic Fatigue Syndrome. Int J Environ Res Public Health. 2021 Oct 13;18(20):10736. doi: 10.3390/ijerph182010736. PMID: 34682478; PMCID: PMC8535251. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8535251/ (Full text)