De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens

Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline which collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo which enable us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients. We reveal that importance of notorious cancer drivers TP53 and PIK3CA fluctuate widely across breast cancers and peak in tumors with distinct numbers of mutations, and that rarely mutated genes such as XPO1 and PLEKHA1 have high disease module importance in specific individuals. Furthermore, individualized module disruption enables us to devise customized singular and combinatorial target therapies which were highly varied across patients demonstrating the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals.

Network medicine for disease module identification and drug repurposing with the NeDRex platform

Traditional drug discovery faces a severe efficacy crisis. Repurposing of registered drugs provides an alternative with lower costs and faster drug development timelines. However, the data necessary for the identification of disease modules, i.e. pathways and sub-networks describing the mechanisms of complex diseases which contain potential drug targets, are scattered across independent databases. Moreover, existing studies are limited to predictions for specific diseases or non-translational algorithmic approaches. There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. We close this gap with NeDRex, an integrative and interactive platform for network-based drug repurposing and disease module discovery. NeDRex integrates ten different data sources covering genes, drugs, drug targets, disease annotations, and their relationships. NeDRex allows for constructing heterogeneous biological networks, mining them for disease modules, prioritizing drugs targeting disease mechanisms, and statistical validation. We demonstrate the utility of NeDRex in five specific use-cases.

Network analysis reveals rare disease signatures across multiple levels of biological organization

Rare genetic diseases are typically caused by a single gene defect. Despite this clear causal relationship between genotype and phenotype, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, we introduce a network approach for evaluating the impact of rare gene defects across biological scales. We construct a multiplex network consisting of over 20 million gene relationships that are organized into 46 network layers spanning six major biological scales between genotype and phenotype. A comprehensive analysis of 3,771 rare diseases reveals distinct phenotypic modules within individual layers. These modules can be exploited to mechanistically dissect the impact of gene defects and accurately predict rare disease gene candidates. Our results show that the disease module formalism can be applied to rare diseases and generalized beyond physical interaction networks. These findings open up new venues to apply network-based tools for cross-scale data integration.

Summer STEM Camp Goes Virtual

Due to the global pandemic of COVID-19, camp and program directors raced to make decisions about summer programming. Traditionally, GSI Summer Camp is a day camp held on a local university campus for four weeks. Despite the disruption caused by the pandemic, the program staff decided to move forward with a seven-week virtual experience for 45 upper elementary, middle, and high school participants. This article presents a description of the implementation of an infectious disease module during a virtual STEM camp.

Impact of COVID 19 on the quality of life OF LIFE (QoL) of patients living with Sickle Cell Disorder (SCD) in Lagos Nigeria

The study aimed to assess the impact of the COVID-19 pandemic on Quality of Life (QoL) in persons living with Sickle Cell Disorder (SCD) in Lagos, Nigeria and to determine how they coped during the pandemic, particularly during the period of total lockdown with the additional SHIELDING measures to which they had to adhere. Data was collected using a standardized protocol PedsQL, Sickle Cell Disease Module version 3, designed for youth within the ages of 13 to 18 years and 19 to 35 years and their parents and guardian if underage. The survey captured data on patients pain impact, hurts, management, treatments, communication with their caregivers and their guardians perception. The survey was performed online, or face to face and telephone interview if online was not possible. Contacts of patients and parents were obtained from the database of Sickle Cell Foundation Nigeria. A total of 105 (80 patients and 25 parents) participants responded to the survey. The age distribution of respondents was highest at 56 percent in the age bracket of 13 to 18 years old. Pain crisis were very common amongst patients. The survey revealed that the type of treatment or care received at these times determined whether or not the patients visited the hospital when they had pain crises. In addition, as patients reports an increase in ill treatment they experienced in the hands of health care givers, so did the fear of accessing treatment during the COVID pandemic. It was observed that the frequency of pain crises experienced by SCD patients was proportional to the patients quality of life (the higher the frequency of pains, the worse the QoL). As a follow-up, a more detailed study would be required, as this study was limited in the capturing of the demographics, sex and number of participants; Considering the number of persons living with SCD that visit the Sickle Cell Foundation Nigeria, (approx. 3,000 patients), the number of responses in this study was low (105). It is believed that a higher number of responses would have given more information about the Sickle Cell burden and the QoL of persons living with SCD in Lagos during the COVID-19 pandemic. Lagos was the epicentre of the COVID-19 pandemic in Nigeria.

Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients

After experiencing the COVID-19 pandemic, it is widely acknowledged that a rapid drug repurposing method is highly needed. A series of useful drug repurposing tools have been developed based on data-driven modeling and network pharmacology. Based on the disease module, we identified several hub proteins that play important roles in the onset and development of the COVID-19, which are potential targets for repositioning approved drugs. Moreover, different network distance metrics were applied to quantify the relationship between drug targets and COVID-19 disease targets in the protein–protein-interaction (PPI) network and predict COVID-19 therapeutic effects of bioactive herbal ingredients and chemicals. Furthermore, the tentative mechanisms of candidates were illustrated through molecular docking and gene enrichment analysis. We obtained 15 chemical and 15 herbal ingredient candidates and found that different drugs may play different roles in the process of virus invasion and the onset and development of the COVID-19 disease. Given pandemic outbreaks, our method has an undeniable immense advantage in the feasibility analysis of drug repurposing or drug screening, especially in the analysis of herbal ingredients.

A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis

Background There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. Result We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10− 47) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. Conclusions We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases.

Integrative analysis of lung molecular signatures reveals key drivers of systemic sclerosis-associated interstitial lung disease

ObjectivesInterstitial lung disease is a significant comorbidity and the leading cause of mortality in patients with systemic sclerosis. Transcriptomic data of systemic sclerosis-associated interstitial lung disease (SSc-ILD) were analysed to evaluate the salient molecular and cellular signatures in comparison with those in related pulmonary diseases and to identify the key driver genes and target molecules in the disease module.MethodsA transcriptomic dataset of lung tissues from patients with SSc-ILD (n=52), idiopathic pulmonary fibrosis (IPF) (n=549), non-specific interstitial pneumonia (n=49) and pulmonary arterial hypertension (n=81) and from normal healthy controls (n=331) was subjected to filtration of differentially expressed genes, functional enrichment analysis, network-based key driver analysis and kernel-based diffusion scoring. The association of enriched pathways with clinical parameters was evaluated in patients with SSc-ILD.ResultsSSc-ILD shared key pathogenic pathways with other fibrosing pulmonary diseases but was distinguishable in some pathological processes. SSc-ILD showed general similarity with IPF in molecular and cellular signatures but stronger signals for myofibroblasts, which in SSc-ILD were in a senescent and apoptosis-resistant state. The p53 signalling pathway was the most enriched signature in lung tissues and lung fibroblasts of SSc-ILD, and was significantly correlated with carbon monoxide diffusing capacity of lung, cellular senescence and apoptosis. EEF2, EFF2K, PHKG2, VCAM1, PRKACB, ITGA4, CDK1, CDK2, FN1 and HDAC1 were key regulators with high diffusion scores in the disease module.ConclusionsIntegrative transcriptomic analysis of lung tissues revealed key signatures of fibrosis in SSc-ILD. A network-based Bayesian approach provides deep insights into key regulatory genes and molecular targets applicable to treating SSc-ILD.

How does onchocerciasis-related skin and eye disease in Africa depend on cumulative exposure to infection and mass treatment?

Background Onchocerciasis (river-blindness) in Africa is targeted for elimination through mass drug administration (MDA) with ivermectin. Onchocerciasis may cause various types of skin and eye disease. Predicting the impact of MDA on onchocercal morbidity is useful for future policy development. Here, we introduce a new disease module within the established ONCHOSIM model to predict trends over time in prevalence of onchocercal morbidity. Methods We developed novel generic model concepts for development of symptoms due to cumulative exposure to dead microfilariae, accommodating both reversible (acute) and irreversible (chronic) symptoms. The model was calibrated to reproduce pre-control age patterns and associations between prevalences of infection, eye disease, and various types of skin disease as observed in a large set of population-based studies. We then used the new disease module to predict the impact of MDA on morbidity prevalence over a 30-year time frame for various scenarios. Results ONCHOSIM reproduced observed age-patterns in disease and community-level associations between infection and disease reasonably well. For highly endemic settings with 30 years of annual MDA at 60% coverage, the model predicted a 70% to 89% reduction in prevalence of chronic morbidity. This relative decline was similar with higher MDA coverage and only somewhat higher for settings with lower pre-control endemicity. The decline in prevalence was lowest for mild depigmentation and visual impairment. The prevalence of acute clinical manifestations (severe itch, reactive skin disease) declined by 95% to 100% after 30 years of annual MDA, regardless of pre-control endemicity. Conclusion We present generic model concepts for predicting trends in acute and chronic symptoms due to history of exposure to parasitic worm infections, and apply this to onchocerciasis. Our predictions suggest that onchocercal morbidity, in particular chronic manifestations, will remain a public health concern in many epidemiological settings in Africa, even after 30 years of MDA.