A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease

Sex-specific skeletal muscle gene expression responses to exercise reveal novel direct mediators of insulin sensitivity change

Understanding how exercise improves whole-body insulin sensitivity (Si) involves complex molecular signaling. This study examines skeletal muscle gene expression changes related to Si, considering sex differences, exercise amount, and intensity to identify pharmacologic targets mimicking exercise benefits. Fifty-three participants from STRRIDE (Studies of Targeted Risk Reduction Interventions through Defined Exercise) I and II completed eight months of aerobic training. Gene expression was assessed via Affymetrix and Illumina technologies, and Si was measured using intravenous glucose tolerance tests. A novel discovery protocol integrating literature-derived and data-driven modeling identified causal pathways and direct transcriptional targets. In women, exercise amount primarily influenced transcription factor targets, which were generally inhibitory, while in men, exercise intensity drove activating targets. Common transcription factors included ATF1, CEBPA, BACH2, and STAT1. Si-related transcriptional targets included TACR3 and TMC7 for intensity-driven effects, and GRIN3B and EIF3B for amount-driven effects. Two key pathways mediating Si improvements were identified: estrogen signaling and protein kinase C (PKC) signaling, both converging on the epidermal growth factor receptor (EGFR) and other relevant targets. The molecular pathways underlying Si improvements varied by sex and exercise parameters, highlighting potential skeletal muscle-specific drug targets such as EGFR to replicate the metabolic benefits of exercise.

Sex-Specific Skeletal Muscle Gene Expression Responses to Exercise Reveal Novel Direct Mediators of Insulin Sensitivity Change

BACKGROUND

Understanding the causal pathways, systems, and mechanisms through which exercise impacts human health is complex. This study explores molecular signaling related to whole-body insulin sensitivity (Si) by examining changes in skeletal muscle gene expression. The analysis considers differences by biological sex, exercise amount, and exercise intensity to identify potential molecular targets for developing pharmacologic agents that replicate the health benefits of exercise.

METHODS

The study involved 53 participants from the STRRIDE I and II trials who completed eight months of aerobic training. Skeletal muscle gene expression was measured using Affymetrix and Illumina technologies, while pre- and post-training Si was assessed via an intravenous glucose tolerance test. A novel gene discovery protocol, integrating three literature-derived and data-driven modeling strategies, was employed to identify causal pathways and direct causal factors based on differentially expressed transcripts associated with exercise intensity and amount.

RESULTS

In women, the transcription factor targets identified were primarily influenced by exercise amount and were generally inhibitory. In contrast, in men, these targets were driven by exercise intensity and were generally activating. Transcription factors such as ATF1, CEBPA, BACH2, and STAT1 were commonly activating in both sexes. Specific transcriptional targets related to exercise-induced Si improvements included TACR3 and TMC7 for intensity-driven effects, and GRIN3B and EIF3B for amount-driven effects. Two key signaling pathways mediating aerobic exercise-induced Si improvements were identified: one centered on estrogen signaling and the other on phorbol ester (PKC) signaling, both converging on the epidermal growth factor receptor (EGFR) and other relevant targets.

CONCLUSIONS

The signaling pathways mediating Si improvements from aerobic exercise differed by sex and were further distinguished by exercise intensity and amount. Transcriptional adaptations in skeletal muscle related to Si improvements appear to be causally linked to estrogen and PKC signaling, with EGFR and other identified targets emerging as potential skeletal muscle-specific drug targets to mimic the beneficial effects of exercise on Si.

Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.

Leveraging genetic diversity to identify small molecules that reverse mouse skeletal muscle insulin resistance

Systems genetics has begun to tackle the complexity of insulin resistance by capitalising on computational advances to study high-diversity populations. 'Diversity Outbred in Australia (DOz)' is a population of genetically unique mice with profound metabolic heterogeneity. We leveraged this variance to explore skeletal muscle's contribution to whole-body insulin action through metabolic phenotyping and skeletal muscle proteomics of 215 DOz mice. Linear modelling identified 553 proteins that associated with whole-body insulin sensitivity (Matsuda Index) including regulators of endocytosis and muscle proteostasis. To enrich for causality, we refined this network by focusing on negatively associated, genetically regulated proteins, resulting in a 76-protein fingerprint of insulin resistance. We sought to perturb this network and restore insulin action with small molecules by integrating the Broad Institute Connectivity Map platform and in vitro assays of insulin action using the Prestwick chemical library. These complementary approaches identified the antibiotic thiostrepton as an insulin resistance reversal agent. Subsequent validation in ex vivo insulin-resistant mouse muscle and palmitate-induced insulin-resistant myotubes demonstrated potent insulin action restoration, potentially via upregulation of glycolysis. This work demonstrates the value of a drug-centric framework to validate systems-level analysis by identifying potential therapeutics for insulin resistance.

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

Bibliometric and visual analysis of cardiovascular diseases and COVID-19 research

Background

The global community has been affected by the coronavirus disease 2019 (COVID-19), which emerged in December 2019. Since then, many studies have been conducted on cardiovascular diseases (CVDs) and COVID-19. The aim of this study was to perform a bibliometric and visual analysis of the published relationship between CVDs and COVID-19.

Methods

1,890 publications were retrieved from the Web of Science Core Collection database on January 5, 2022. Microsoft Office Excel and CiteSpace were then used to carry out scientometric analysis on the relevant literature according to seven aspects: document type, countries/regions, institutions, authors, journals, references, and keywords.

Results

The research on CVDs and COVID-19 is currently in a period of rapid development, with China, USA, England, and Italy leading the field. There is active cooperation between most countries and institutions. Harvard Medical School stands out among the many institutions not only for the largest number of publications, but also for their high quality. Banerjee A, Solomon SD and Narula J are three representative authors in this field. Frontiers in Cardiovascular Medicine was the journal with the highest number of published studies, and The Lancet was the most cited journal. Two documents with a high degree of significance in this field were identified. Popular research topics in this field are specific diseases, such as acute coronary syndrome and heart failure; pathogenesis related to ACE2, insulin resistance and pericyte; the specific therapeutic drug chloroquine; and clinical characteristics, physical activity, and mental health. ACE2 and NF-κB will be the focus of future research.

Conclusions

This study provides useful information for the research of CVDs and COVID-19, including potential collaborators, popular research topics, and a reference for more extensive and in-depth research in the future.

Gene expression profile suggests different mechanisms underlying sporadic and familial mesial temporal lobe epilepsy

Most patients with pharmacoresistant mesial temporal lobe epilepsy (MTLE) have hippocampal sclerosis on the postoperative histopathological examination. Although most patients with MTLE do not refer to a family history of the disease, familial forms of MTLE have been reported. We studied surgical specimens from patients with MTLE who had epilepsy surgery for medically intractable seizures. We assessed and compared gene expression profiles of the tissue lesion found in patients with familial MTLE (n = 3) and sporadic MTLE (n = 5). In addition, we used data from control hippocampi obtained from a public database (n = 7). We obtained expression profiles using the Human Genome U133 Plus 2.0 (Affymetrix) microarray platform. Overall, the molecular profile identified in familial MTLE differed from that in sporadic MTLE. In the tissue of patients with familial MTLE, we found an over-representation of the biological pathways related to protein response, mRNA processing, and synaptic plasticity and function. In sporadic MTLE, the gene expression profile suggests that the inflammatory response is highly activated. In addition, we found enrichment of gene sets involved in inflammatory cytokines and mediators and chemokine receptor pathways in both groups. However, in sporadic MTLE, we also found enrichment of epidermal growth factor signaling, prostaglandin synthesis and regulation, and microglia pathogen phagocytosis pathways. Furthermore, based on the gene expression signatures, we identified different potential compounds to treat patients with familial and sporadic MTLE. To our knowledge, this is the first study assessing the mRNA profile in surgical tissue obtained from patients with familial MTLE and comparing it with sporadic MTLE. Our results clearly show that, despite phenotypic similarities, both forms of MTLE present distinct molecular signatures, thus suggesting different underlying molecular mechanisms that may require distinct therapeutic approaches.

Whole blood transcriptomic profiling identifies molecular pathways related to cardiovascular mortality in heart failure

AIMS

Chronic heart failure (CHF) is a systemic syndrome with a poor prognosis and a need for novel therapies. We investigated whether whole blood transcriptomic profiling can provide new mechanistic insights into cardiovascular (CV) mortality in CHF.

METHODS AND RESULTS

Transcriptome profiles were generated at baseline from 944 CHF patients from the BIOSTAT-CHF study, of whom 626 survived and 318 died from a CV cause during a follow-up of 21 months. Multivariable analysis, including adjustment for cell count, identified 1153 genes (6.5%) that were differentially expressed between those that survived or died and strongly related to a validated clinical risk score for adverse prognosis. The differentially expressed genes mainly belonged to five non-redundant pathways: adaptive immune response, proteasome-mediated ubiquitin-dependent protein catabolic process, T-cell co-stimulation, positive regulation of T-cell proliferation, and erythrocyte development. These five pathways were selectively related (RV coefficients >0.20) with seven circulating protein biomarkers of CV mortality (fibroblast growth factor 23, soluble ST2, adrenomedullin, hepcidin, pentraxin-3, WAP 4-disulfide core domain 2, and interleukin-6) revealing an intricate relationship between immune and iron homeostasis. The pattern of survival-associated gene expression matched with 29 perturbagen-induced transcriptome signatures in the iLINCS drug-repurposing database, identifying drugs, approved for other clinical indications, that were able to reverse in vitro the molecular changes associated with adverse prognosis in CHF.

CONCLUSION

Systematic modelling of the whole blood protein-coding transcriptome defined molecular pathways that provide a link between clinical risk factors and adverse CV prognosis in CHF, identifying both established and new potential therapeutic targets.