muSignAl: An algorithm to search for multiple omic signatures with similar predictive performance

Multidimensional omic datasets often have correlated features leading to the possibility of discovering multiple biological signatures with similar predictive performance for a phenotype. However, their exploration is limited by low sample size and the exponential nature of the combinatorial search leading to high computational cost. To address these issues, we have developed an algorithm muSignAl (multiple signature algorithm) which selects multiple signatures with similar predictive performance while systematically bypassing the requirement of exploring all the combinations of features. We demonstrated the workflow of this algorithm with an example of proteomics dataset. muSignAl is applicable in various bioinformatics driven explorations, such as understanding the relationship between multiple biological feature sets and phenotypes, and discovery and development of biomarker panels while providing the opportunity of optimising their development cost with the help of equally good multiple signatures. Source code of muSignAl is freely available at https://github.com/ShuklaLab/muSignAl

Data-driven patient stratification of UK Biobank cohort suggests five endotypes of multimorbidity

Multimorbidity generally refers to concurrent occurrence of multiple chronic conditions. These patients are inherently at high risk and often lead a poor quality of life due to delayed treatments. With the emergence of personalized medicine and stratified healthcare, there is a need to stratify patients right at the primary care setting. Here we developed multimorbidity analysis pipeline (MulMorPip), which can stratify patients into multimorbid subgroups or endotypes based on their lifetime disease diagnosis and characterize them based on demographic features and underlying disease–disease interaction networks. By implementing MulMorPip on UK Biobank cohort, we report five distinct molecular subclasses or endotypes of multimorbidity. For each patient, we calculated the existence of broad disease classes defined by Charlson's comorbidity classification using the International Classification of Diseases-10 encoding. We then applied multiple correspondence analysis in 77 524 patients from UK Biobank, who had multimorbidity of more than one disease, which resulted in five multimorbid clusters. We further validated these clusters using machine learning and were able to classify 20% model-blind test set patients with an accuracy of 97% and an average Jaccard similarity of 84%. This was followed by demographic characterization and development of interlinking disease network for each cluster to understand disease–disease interactions. Our identified five endotypes of multimorbidity draw attention to dementia, stroke and paralysis as important drivers of multimorbidity stratification. Inclusion of such patient stratification at the primary care setting can help general practitioners to better observe patients’ multiple chronic conditions, their risk stratification and personalization of treatment strategies

An AI Approach to Identifying Novel Therapeutics for Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that has a significant impact on quality of life and work capacity. Treatment of RA aims to control inflammation and alleviate pain; however, achieving remission with minimal toxicity is frequently not possible with the current suite of drugs. This review aims to summarise current treatment practices and highlight the urgent need for alternative pharmacogenomic approaches for novel drug discovery. These approaches can elucidate new relationships between drugs, genes, and diseases to identify additional effective and safe therapeutic options. This review discusses how computational approaches such as connectivity mapping offer the ability to repurpose FDA-approved drugs beyond their original treatment indication. This review also explores the concept of drug sensitisation to predict co-prescribed drugs with synergistic effects that produce enhanced anti-disease efficacy by involving multiple disease pathways. Challenges of this computational approach are discussed, including the availability of suitable high-quality datasets for comprehensive analysis and other data curation issues. The potential benefits include accelerated identification of novel drug combinations and the ability to trial and implement established treatments in a new index disease. This review underlines the huge opportunity to incorporate disease-related data and drug-related data to develop methods and algorithms that have strong potential to determine novel and effective treatment regimens

Propionibacterium acnes and acne vulgaris: new insights from the integration of population genetic, multi-omic, biochemical and host-microbe studies.

The anaerobic bacterium Propionibacterium acnes is believed to play an important role in the pathophysiology of the common skin disease acne vulgaris. Over the last 10 years our understanding of the taxonomic and intraspecies diversity of this bacterium has increased tremendously, and with it the realisation that particular strains are associated with skin health while others appear related to disease. This extensive review will cover our current knowledge regarding the association of P. acnes phylogroups, clonal complexes and sequence types with acne vulgaris based on multilocus sequence typing of isolates, and direct ribotyping of the P. acnes strain population in skin microbiome samples based on 16S rDNA metagenomic data. We will also consider how multi-omic and biochemical studies have facilitated our understanding of P. acnes pathogenicity and interactions with the host, thus providing insights into why certain lineages appear to have a heightened capacity to contribute to acne vulgaris development, while others are positively associated with skin health. We conclude with a discussion of new therapeutic strategies that are currently under investigation for acne vulgaris, including vaccination, and consider the potential of these treatments to also perturb beneficial lineages of P. acnes on the skin

The Need for Standardizing Diagnosis, Treatment and Clinical Care of Cholecystitis and Biliary Colic in Gallbladder Disease

Gallstones affect 20% of the Western population and will grow in clinical significance as obesity and metabolic diseases become more prevalent. Gallbladder removal (cholecystectomy) is a common treatment for diseases caused by gallstones, with 1.2 million surgeries in the US each year, each costing USD 10,000. Gallbladder disease has a significant impact on the logistics and economics of healthcare. We discuss the two most common presentations of gallbladder disease (biliary colic and cholecystitis) and their pathophysiology, risk factors, signs and symptoms. We discuss the factors that affect clinical care, including diagnosis, treatment outcomes, surgical risk factors, quality of life and cost-efficacy. We highlight the importance of standardised guidelines and objective scoring systems in improving quality, consistency and compatibility across healthcare providers and in improving patient outcomes, collaborative opportunities and the cost-effectiveness of treatment. Guidelines and scoring only exist in select areas of the care pathway. Opportunities exist elsewhere in the care pathway

TACE/ADAM17 substrates associate with ACS (Ep-CAM, HB-EGF) and follow-up MACE (TNFR1 and TNFR2)

BACKGROUND AND AIMS: TACE/ADAM17 is a membrane bound metalloprotease, which cleaves substrates involved in immune and inflammatory responses and plays a role in coronary artery disease (CAD). We measured TACE and its substrates in CAD patients to identify potential biomarkers within this molecular pathway with potential for acute coronary syndrome (ACS) and major adverse cardiovascular events (MACE) prediction. METHODS: Blood samples were obtained from consecutive patients (n = 229) with coronary angiographic evidence of CAD admitted with ACS or electively. MACE were recorded after a median 3-year follow-up. Controls (n = 115) had a <10% CAD risk as per the HeartSCORE. TACE and TIMP3 protein and mRNA levels were measured by ELISA and RT-qPCR respectively. TACE substrates were measured using a multiplex proximity extension assay. RESULTS: TACE mRNA and cell protein levels (p < 0.01) and TACE substrates LDLR (p = 0.006), TRANCE (p = 0.045), LAG-3 (p < 0.001) and ACE2 (p < 0.001) plasma levels were significantly higher in CAD patients versus controls. TACE inhibitor TIMP3 mRNA levels were significantly lower in CAD patients and tended to be lower in the ACS population (p < 0.05). TACE substrates TNFR1 (OR:3.237,CI:1.514–6.923,p = 0.002), HB-EGF (OR:0.484,CI:0.288–0.813,p = 0.006) and Ep-CAM (OR:0.555,CI:0.327–0.829,p = 0.004) accurately classified ACS patients with HB-EGF and Ep-CAM levels being lower compared to electively admitted patients. TNFR1 (OR:2.317,CI:1.377–3.898,p = 0.002) and TNFR2 (OR:1.902,CI:1.072–3.373,p = 0.028) were significantly higher on admission in those patients who developed MACE within 3 years. CONCLUSIONS: We demonstrate a possible role of TACE substrates LAG-3, HB-EGF and Ep-CAM in atherosclerotic plaque development and stability. We also underline the importance of measuring TNFR1 and TNFR2 earlier than previously appreciated for MACE prediction. We report an important role of TIMP3 in regulating TACE levels