Time-Resolved Systems Medicine Reveals Viral Infection-Modulating Host Targets

Phospholipid scramblase 1: a frontline defense against viral infections

Phospholipid scramblase 1 (PLSCR1) is the most studied member of the phospholipid scramblase protein family. Its main function is to catalyze calcium (Ca2+)-dependent, ATP-independent, bidirectional and non-specific translocation of phospholipids between inner and outer leaflets of plasma membrane. Additionally, PLSCR1 is identified as an interferon-stimulated gene (ISG) with antiviral activities, and its expression can be highly induced by all types of interferons in various viral infections. Indeed, numerous studies have reported the direct antiviral activities of PLSCR1 through interrupting the replication processes of a variety of viruses, including entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), nuclear localization of influenza A virus (IAV), and transactivation of human immunodeficiency virus (HIV), Epstein-Barr virus (EBV), human T-cell leukemia virus type-1 (HTLV1), human cytomegalovirus (HCMV) and hepatitis B virus (HBV). In addition to these direct antiviral activities, PLSCR1 also regulates endogenous immune components to defend against viruses in both nonimmune and immune cells. Such activities include potentiation of ISG transcription, activation of JAK/STAT pathway, upregulation of type 3 interferon receptor (IFN-λR1) and recruitment of Toll-like receptor 9 (TLR9). This review aims to summarize the current understanding of PLSCR1's multiple roles as a frontline defense against viral infections.

Integrated transcriptomic analysis of human induced pluripotent stem cell-derived osteogenic differentiation reveals a regulatory role of KLF16

Osteogenic differentiation is essential for bone development, metabolism, and repair; however, the underlying regulatory relationships among genes remain poorly understood. To elucidate the transcriptomic changes and identify novel regulatory genes involved in osteogenic differentiation, we differentiated mesenchymal stem cells (MSCs) derived from 20 human iPSC lines into preosteoblasts (preOBs) and osteoblasts (OBs). We then performed transcriptome profiling of MSCs, preOBs and OBs. The iPSC-derived MSCs and OBs showed similar transcriptome profiles to those of primary human MSCs and OBs, respectively. Differential gene expression analysis revealed global changes in the transcriptomes from MSCs to preOBs, and then to OBs, including the differential expression of 840 genes encoding transcription factors (TFs). TF regulatory network analysis uncovered a network comprising 451 TFs, organized into five interactive modules. Multiscale embedded gene co-expression network analysis (MEGENA) identified gene co-expression modules and key network regulators (KNRs). From these analyses, KLF16 emerged as an important TF in osteogenic differentiation. We demonstrate that overexpression of Klf16 in vitro inhibited osteogenic differentiation and mineralization, while Klf16 +/- mice exhibited increased bone mineral density, trabecular number, and cortical bone area. Our study underscores the complexity of osteogenic differentiation and identifies novel regulatory genes such as KLF16, which plays an inhibitory role in osteogenic differentiation both in vitro and in vivo.

Antiviral responses versus virus-induced cellular shutoff: a game of thrones between influenza A virus NS1 and SARS-CoV-2 Nsp1

Following virus recognition of host cell receptors and viral particle/genome internalization, viruses replicate in the host via hijacking essential host cell machinery components to evade the provoked antiviral innate immunity against the invading pathogen. Respiratory viral infections are usually acute with the ability to activate pattern recognition receptors (PRRs) in/on host cells, resulting in the production and release of interferons (IFNs), proinflammatory cytokines, chemokines, and IFN-stimulated genes (ISGs) to reduce virus fitness and mitigate infection. Nevertheless, the game between viruses and the host is a complicated and dynamic process, in which they restrict each other via specific factors to maintain their own advantages and win this game. The primary role of the non-structural protein 1 (NS1 and Nsp1) of influenza A viruses (IAV) and the pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), respectively, is to control antiviral host-induced innate immune responses. This review provides a comprehensive overview of the genesis, spatial structure, viral and cellular interactors, and the mechanisms underlying the unique biological functions of IAV NS1 and SARS-CoV-2 Nsp1 in infected host cells. We also highlight the role of both non-structural proteins in modulating viral replication and pathogenicity. Eventually, and because of their important role during viral infection, we also describe their promising potential as targets for antiviral therapy and the development of live attenuated vaccines (LAV). Conclusively, both IAV NS1 and SARS-CoV-2 Nsp1 play an important role in virus-host interactions, viral replication, and pathogenesis, and pave the way to develop novel prophylactic and/or therapeutic interventions for the treatment of these important human respiratory viral pathogens.

Systems Immunology Approaches to Metabolism

Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.

Integrative Analysis of the Ethanol Tolerance of Saccharomyces cerevisiae

Ethanol (EtOH) alters many cellular processes in yeast. An integrated view of different EtOH-tolerant phenotypes and their long noncoding RNAs (lncRNAs) is not yet available. Here, large-scale data integration showed the core EtOH-responsive pathways, lncRNAs, and triggers of higher (HT) and lower (LT) EtOH-tolerant phenotypes. LncRNAs act in a strain-specific manner in the EtOH stress response. Network and omics analyses revealed that cells prepare for stress relief by favoring activation of life-essential systems. Therefore, longevity, peroxisomal, energy, lipid, and RNA/protein metabolisms are the core processes that drive EtOH tolerance. By integrating omics, network analysis, and several other experiments, we showed how the HT and LT phenotypes may arise: (1) the divergence occurs after cell signaling reaches the longevity and peroxisomal pathways, with CTA1 and ROS playing key roles; (2) signals reaching essential ribosomal and RNA pathways via SUI2 enhance the divergence; (3) specific lipid metabolism pathways also act on phenotype-specific profiles; (4) HTs take greater advantage of degradation and membraneless structures to cope with EtOH stress; and (5) our EtOH stress-buffering model suggests that diauxic shift drives EtOH buffering through an energy burst, mainly in HTs. Finally, critical genes, pathways, and the first models including lncRNAs to describe nuances of EtOH tolerance are reported here.

AngClust: Angle Feature-Based Clustering for Short Time Series Gene Expression Profiles

When clustering gene expression, it is expected that correlation coefficients of genes in the same clusters are high, and that gene ontology (GO) enrichment analysis of most clusters will be significant. However, existing short-term gene expression clustering algorithms have limitations. To address this problem, we proposed a novel clustering process based on angular features for short-term gene expression. Our method (named AngClust) uses angular features to indicate the change of trend in gene expression levels at two neighboring time points. The changes of angles at multiple time points reflects the change of trend of the overall expression levels. Such changes are used to measure whether the expression trends of different genes are similar. To obtain functionally significant clusters from the clustering results, we evaluated numbers of genes in clusters, average correlation coefficient, fluctuation, and their correlation with GO term enrichment. The efficacy of AngClust outperform two other measures, Euclidean distance (ED) and dynamic time warping of correlation (DTW), on a dataset of yeast gene expression. The ratios of GO and pathway term-enriched of clusters of AngClust is higher than or equal to that of STEM and TMixClust on human, mouse, and yeast time series of gene expression.

High-fat diet-induced obesity augments the deleterious effects of estrogen deficiency on bone: Evidence from ovariectomized mice

Several epidemiological studies have suggested that obesity complicated with insulin resistance and type 2 diabetes exerts deleterious effects on the skeleton. While obesity coexists with estrogen deficiency in postmenopausal women, their combined effects on the skeleton are poorly studied. Thus, we investigated the impact of high-fat diet (HFD) on bone and metabolism of ovariectomized (OVX) female mice (C57BL/6J). OVX or sham operated mice were fed either HFD (60%fat) or normal diet (10%fat) for 12 weeks. HFD-OVX group exhibited pronounced increase in body weight (~86% in HFD and ~122% in HFD-OVX, p < 0.0005) and impaired glucose tolerance. Bone microCT-scanning revealed a pronounced decrease in trabecular bone volume/total volume (BV/TV) (-15.6 ± 0.48% in HFD and -37.5 ± 0.235% in HFD-OVX, p < 0.005) and expansion of bone marrow adipose tissue (BMAT; +60.7 ± 9.9% in HFD vs. +79.5 ± 5.86% in HFD-OVX, p < 0.005). Mechanistically, HFD-OVX treatment led to upregulation of genes markers of senescence, bone resorption, adipogenesis, inflammation, downregulation of gene markers of bone formation and bone development. Similarly, HFD-OVX treatment resulted in significant changes in bone tissue levels of purine/pyrimidine and Glutamate metabolisms, known to play a regulatory role in bone metabolism. Obesity and estrogen deficiency exert combined deleterious effects on bone resulting in accelerated cellular senescence, expansion of BMAT and impaired bone formation leading to decreased bone mass. Our results suggest that obesity may increase bone fragility in postmenopausal women.

Federated Random Forests can improve local performance of predictive models for various healthcare applications

MOTIVATION

Limited data access has hindered the field of precision medicine from exploring its full potential, e.g. concerning machine learning and privacy and data protection rules.Our study evaluates the efficacy of federated Random Forests (FRF) models, focusing particularly on the heterogeneity within and between datasets. We addressed three common challenges: (i) number of parties, (ii) sizes of datasets and (iii) imbalanced phenotypes, evaluated on five biomedical datasets.

RESULTS

The FRF outperformed the average local models and performed comparably to the data-centralized models trained on the entire data. With an increasing number of models and decreasing dataset size, the performance of local models decreases drastically. The FRF, however, do not decrease significantly. When combining datasets of different sizes, the FRF vastly improve compared to the average local models. We demonstrate that the FRF remain more robust and outperform the local models by analyzing different class-imbalances.Our results support that FRF overcome boundaries of clinical research and enables collaborations across institutes without violating privacy or legal regulations. Clinicians benefit from a vast collection of unbiased data aggregated from different geographic locations, demographics and other varying factors. They can build more generalizable models to make better clinical decisions, which will have relevance, especially for patients in rural areas and rare or geographically uncommon diseases, enabling personalized treatment. In combination with secure multi-party computation, federated learning has the power to revolutionize clinical practice by increasing the accuracy and robustness of healthcare AI and thus paving the way for precision medicine.

AVAILABILITY AND IMPLEMENTATION

The implementation of the federated random forests can be found at https://featurecloud.ai/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Fostering reproducibility, reusability, and technology transfer in health informatics

Computational methods can transform healthcare. In particular, health informatics with artificial intelligence has shown tremendous potential when applied in various fields of medical research and has opened a new era for precision medicine. The development of reusable biomedical software for research or clinical practice is time-consuming and requires rigorous compliance with quality requirements as defined by international standards.

However, research projects rarely implement such measures, hindering smooth technology transfer into the research community or manufacturers as well as reproducibility and reusability.

Here, we present a guideline for quality management systems (QMS) for academic organizations incorporating the essential components while confining the requirements to an easily manageable effort. It provides a starting point to implement a QMS tailored to specific needs effortlessly and greatly facilitates technology transfer in a controlled manner, thereby supporting reproducibility and reusability.

Ultimately, the emerging standardized workflows can pave the way for an accelerated deployment in clinical practice.

Transcriptome and lipidome profile of human mesenchymal stem cells with reduced senescence and increased trilineage differentiation ability upon drug treatment

Human Mesenchymal stem cells (hMSCs) are multi-potential cells which are widely used in cell therapy. However, the frequently emerged senescence and decrease of differentiation capabilities limited the broad applications of MSC. Several strategies such as small molecules treatment have been widely studied and used to improve the stem characteristics bypassing the senescence but the exact mechanisms for them to reduce senescence have not been fully studied. In this study, hMSCs were treated by rapamycin, oltipraz, metformin, and vitamin C for the indicated time and these cells were subjected to senescence evaluation and trilineage differentiation. Furthermore, transcriptomics and lipidomics datasets of hMSCs after drug treatment were analyzed to interpret biological pathways responsible for their anti-senescence effects. Although four drugs exhibited significant activities in promoting MSC osteogenic differentiation, metformin is the optimal drug to promote trilineage differentiation. GO terms illustrated that the anti-aging effects of drugs were mainly associated with cellular senescence, mitotic and meiosis process. Biosynthesis of phosphatidylcholines (PC) and phosphatidylethanolamine (PE) were inhibited whereas production of phosphatidylinositols (PIs) and saturated fatty acids (SFA)/ mono-unsaturated fatty acids (MUFA) conversion was activated. Medium free fatty acids (FFA) was increased in hMSCs with different anti-aging phenotypes. Therefore, we established a comprehensive method in assessing drug intervention based on the results of transcriptomics and lipidomics. The method can be used to study different biological phenotypes upon drug intervention in MSC which will extend the clinical application of hMSCs.