Machine learning uncovers cell identity regulator by histone code

Early tolerance and late persistence as alternative drug responses in cancer

Bacteria withstand antibiotic treatment through three alternative mechanisms: resistance, persistence or tolerance. While resistance and persistence have been described, whether drug-induced tolerance exists in cancer cells remains largely unknown. Here, we show that human cancer cells elicit a tolerant response when exposed to commonly used chemotherapy regimens, propelled by the pervasive activation of autophagy, leading to the comprehensive activation of DNA damage repair pathways. After prolonged drug exposure, such tolerant responses morph into persistence, whereby the increased DNA damage repair is entirely reversed. The central regulator of mitophagy PINK1 drives this reduction in DNA repair via the cytoplasmic relocalization of the cell identity master HNF4A, thus hampering HNF4A transcriptional activation of DNA repair genes. We conclude that exposing cancer cells to relevant standard-of-care antitumour therapies induces a pervasive drug-induced tolerant response that might be broadly exploited to increase the impact of first-line, adjuvant treatments and debulking in advanced cancers.

Machine Learning Uncovers Vascular Endothelial Cell Identity Genes by Expression Regulation Features in Single Cells

Deciphering cell identity genes is pivotal to understanding cell differentiation, development, and many diseases involving cell identity dysregulation. Here, we introduce SCIG, a machine-learning method to uncover cell identity genes in single cells. In alignment with recent reports that cell identity genes are regulated with unique epigenetic signatures, we found cell identity genes exhibit distinctive genetic sequence signatures, e.g., unique enrichment patterns of cis-regulatory elements. Using these genetic sequence signatures, along with gene expression information from single-cell RNA-seq data, enables SCIG to uncover the identity genes of a cell without a need for comparison to other cells. Cell identity gene score defined by SCIG surpassed expression value in network analysis to uncover master transcription factors regulating cell identity. Applying SCIG to the human endothelial cell atlas revealed that the tissue microenvironment is a critical supplement to master transcription factors for cell identity refinement. SCIG is publicly available at https://github.com/kaifuchenlab/SCIG , offering a valuable tool for advancing cell differentiation, development, and regenerative medicine research.

Determinants of Chromatin Organization in Aging and Cancer-Emerging Opportunities for Epigenetic Therapies and AI Technology

This review article critically examines the pivotal role of chromatin organization in gene regulation, cellular differentiation, disease progression and aging. It explores the dynamic between the euchromatin and heterochromatin, coded by a complex array of histone modifications that orchestrate essential cellular processes. We discuss the pathological impacts of chromatin state misregulation, particularly in cancer and accelerated aging conditions such as progeroid syndromes, and highlight the innovative role of epigenetic therapies and artificial intelligence (AI) in comprehending and harnessing the histone code toward personalized medicine. In the context of aging, this review explores the use of AI and advanced machine learning (ML) algorithms to parse vast biological datasets, leading to the development of predictive models for epigenetic modifications and providing a framework for understanding complex regulatory mechanisms, such as those governing cell identity genes. It supports innovative platforms like CEFCIG for high-accuracy predictions and tools like GridGO for tailored ChIP-Seq analysis, which are vital for deciphering the epigenetic landscape. The review also casts a vision on the prospects of AI and ML in oncology, particularly in the personalization of cancer therapy, including early diagnostics and treatment optimization for diseases like head and neck and colorectal cancers by harnessing computational methods, AI advancements and integrated clinical data for a transformative impact on healthcare outcomes.

Low RNA stability signifies strong expression regulatability of tumor suppressors

RNA expression of a gene is determined by not only transcriptional regulation, but also post-transcriptional regulation of RNA decay. The precise regulation of RNA stability in the cell plays an important role in normal development. Dysregulation of RNA stability can lead to diseases such as cancer. Here we found tumor suppressor RNAs tended to decay fast in normal cell types when compared with other RNAs. Consistent with a negative effect of m6A modification on RNA stability, we observed preferential deposition of m6A on tumor suppressor RNAs. Moreover, abundant m6A and fast decay of tumor suppressor RNAs both tended to be further enhanced in prostate cancer cells relative to normal prostate epithelial cells. Further, knockdown of m6A methyltransferase METTL3 and reader YTHDF2 in prostate cancer cells both posed stronger effect on tumor suppressor RNAs than on other RNAs. These results indicated a strong post transcriptional expression regulatability mediated by abundant m6A modification on tumor suppressor RNAs.

Extrachromosomal DNA (ecDNA): Unveiling its role in cancer progression and implications for early detection

Extrachromosomal DNA (ecDNA) is a special class of circular DNA in eukaryotes, which is independent of conventional chromosomes. These circular molecules play important roles in biology, especially in cancer biology. The emergence of sequencing technologies such as CCDA-seq and Amplicon Architect has led to a progressive unraveling of the mystery of ecDNA. Consequently, insights into its function and potential applications have begun to surface. Among these studies, the most noteworthy research pertains to cancer-related investigations into ecDNA. Numerous studies have underscored the significance of ecDNA in the pathogenesis of cancer and its role in accelerating cancer evolution. This review provides an overview of the source, structure, and function of ecDNA, while compiling recent advancements in ecDNA in the field of cancer. Nonetheless, further research is imperative to determine its effectiveness and specificity as a biomarker for early cancer detection.

Tbx5 maintains atrial identity in post-natal cardiomyocytes by regulating an atrial-specific enhancer network

Understanding how the atrial and ventricular heart chambers maintain distinct identities is a prerequisite for treating chamber-specific diseases. Here, we selectively knocked out (KO) the transcription factor Tbx5 in the atrial working myocardium to evaluate its requirement for atrial identity. Atrial Tbx5 inactivation downregulated atrial cardiomyocyte (aCM) selective gene expression. Using concurrent single nucleus transcriptome and open chromatin profiling, genomic accessibility differences were identified between control and Tbx5 KO aCMs, revealing that 69% of the control-enriched ATAC regions were bound by TBX5. Genes associated with these regions were downregulated in KO aCMs, suggesting they function as TBX5-dependent enhancers. Comparing enhancer chromatin looping using H3K27ac HiChIP identified 510 chromatin loops sensitive to TBX5 dosage, and 74.8% of control-enriched loops contained anchors in control-enriched ATAC regions. Together, these data demonstrate TBX5 maintains the atrial gene expression program by binding to and preserving the tissue-specific chromatin architecture of atrial enhancers.

Low RNA stability signifies increased post-transcriptional regulation of cell identity genes

Cell identity genes are distinct from other genes with respect to the epigenetic mechanisms to activate their transcription, e.g. by super-enhancers and broad H3K4me3 domains. However, it remains unclear whether their post-transcriptional regulation is also unique. We performed a systematic analysis of transcriptome-wide RNA stability in nine cell types and found that unstable transcripts were enriched in cell identity-related pathways while stable transcripts were enriched in housekeeping pathways. Joint analyses of RNA stability and chromatin state revealed significant enrichment of super-enhancers and broad H3K4me3 domains at the gene loci of unstable transcripts. Intriguingly, the RNA m6A methyltransferase, METTL3, preferentially binds to chromatin at super-enhancers, broad H3K4me3 domains and their associated genes. METTL3 binding intensity is positively correlated with RNA m6A methylation and negatively correlated with RNA stability of cell identity genes, probably due to co-transcriptional m6A modifications promoting RNA decay. Nanopore direct RNA-sequencing showed that METTL3 knockdown has a stronger effect on RNA m6A and mRNA stability for cell identity genes. Our data suggest a run-and-brake model, where cell identity genes undergo both frequent transcription and fast RNA decay to achieve precise regulation of RNA expression.

Epigenetic landscape reveals MECOM as an endothelial lineage regulator

A comprehensive understanding of endothelial cell lineage specification will advance cardiovascular regenerative medicine. Recent studies found that unique epigenetic signatures preferentially regulate cell identity genes. We thus systematically investigate the epigenetic landscape of endothelial cell lineage and identify MECOM to be the leading candidate as an endothelial cell lineage regulator. Single-cell RNA-Seq analysis verifies that MECOM-positive cells are exclusively enriched in the cell cluster of bona fide endothelial cells derived from induced pluripotent stem cells. Our experiments demonstrate that MECOM depletion impairs human endothelial cell differentiation, functions, and Zebrafish angiogenesis. Through integrative analysis of Hi-C, DNase-Seq, ChIP-Seq, and RNA-Seq data, we find MECOM binds enhancers that form chromatin loops to regulate endothelial cell identity genes. Further, we identify and verify the VEGF signaling pathway to be a key target of MECOM. Our work provides important insights into epigenetic regulation of cell identity and uncovered MECOM as an endothelial cell lineage regulator.

Histone Deacetylase 1 Expression and Regulatory Network in Lung Adenocarcinoma Based on Data Mining and Implications for Targeted Treatment

Background and Aims

Histone deacetylase 1 (HDAC1) codes a protein that is a component of the histone deacetylase complex. The abnormal expression of HDAC1 is strongly correlated with cell proliferation, differentiation, transcription, and translation. Through continuous screening of genes associated with changes in lung adenocarcinoma (LUAD), gene networks are formed to explore tumor pathogenesis and new therapeutic targets.

Methods

We evaluated HDAC1 gene survival analysis and its expression of LUAD using relevant websites and databases (TCGA and GEO databases). Through data mining, we determined the frequency and type of HDAC1 mutation, obtained the relevant heat map of the gene interaction network, completed the analysis of gene ontology and function enrichment, and understood the pharmaceutic of HDAC1.

Results

We found that HDAC1 expression was associated with the prognosis of patients with LUAD. In gene expression analysis, HDAC1 was highly expressed in LUAD, and the HDAC1 interaction gene network (MARCKSL, eIF3I) was closely related to cellular gene expression. Functional network analysis shows that the expression of HDAC1 is related to the monitoring point of the G1-S phase of the cell cycle and the activation of the Notch signaling pathway (CSL transcription factor), which is involved in the process of cell proliferation and differentiation and gene expression associated with new therapeutic targets.

Conclusion

Our data revealed the expression and potential regulatory factors of HDAC1 in LUAD of data mining, which laid a foundation for the study of the occurrence, development, and treatment of HDAC1 in LUAD.

Prognostic model and immunotherapy prediction based on molecular chaperone-related lncRNAs in lung adenocarcinoma

Introduction: Molecular chaperones and long non-coding RNAs (lncRNAs) have been confirmed to be closely related to the occurrence and development of tumors, especially lung cancer. Our study aimed to construct a kind of molecular chaperone-related long non-coding RNAs (MCRLncs) marker to accurately predict the prognosis of lung adenocarcinoma (LUAD) patients and find new immunotherapy targets.

Methods: In this study, we acquired molecular chaperone genes from two databases, Genecards and molecular signatures database (MsigDB). And then, we downloaded transcriptome data, clinical data, and mutation information of LUAD patients through the Cancer Genome Atlas (TCGA). MCRLncs were determined by Spearman correlation analysis. We used univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to construct risk models. Kaplan-meier (KM) analysis was used to understand the difference in survival between high and low-risk groups. Nomogram, calibration curve, concordance index (C-index) curve, and receiver operating characteristic (ROC) curve were used to evaluate the accuracy of the risk model prediction. In addition, we used gene ontology (GO) enrichment analysis and kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses to explore the potential biological functions of MCRLncs. Immune microenvironmental landscapes were constructed by using single-sample gene set enrichment analysis (ssGSEA), tumor immune dysfunction and exclusion (TIDE) algorithm, “pRRophetic” R package, and “IMvigor210” dataset. The stem cell index based on mRNAsi expression was used to further evaluate the patient’s prognosis.

Results: Sixteen MCRLncs were identified as independent prognostic indicators in patients with LUAD. Patients in the high-risk group had significantly worse overall survival (OS). ROC curve suggested that the prognostic features of MCRLncs had a good predictive ability for OS. Immune system activation was more pronounced in the high-risk group. Prognostic features of the high-risk group were strongly associated with exclusion and cancer-associated fibroblasts (CAF). According to this prognostic model, a total of 15 potential chemotherapeutic agents were screened for the treatment of LUAD. Immunotherapy analysis showed that the selected chemotherapeutic drugs had potential application value. Stem cell index mRNAsi correlates with prognosis in patients with LUAD.

Conclusion: Our study established a kind of novel MCRLncs marker that can effectively predict OS in LUAD patients and provided a new model for the application of immunotherapy in clinical practice.

Assessment and Optimization of Explainable Machine Learning Models Applied to Transcriptomic Data

Explainable artificial intelligence aims to interpret how machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, understanding of the applicability of these model explainers to biological data is still lacking. In this study, we comprehensively evaluated multiple explainers by interpreting pre-trained models for predicting tissue types from transcriptomic data and by identifying the top contributing genes from each sample with the greatest impacts on model prediction. To improve the reproducibility and interpretability of results generated by model explainers, we proposed a series of optimization strategies for each explainer on two different model architectures of multilayer perceptron (MLP) and convolutional neural network (CNN). We observed three groups of explainer and model architecture combinations with high reproducibility. Group II, which contains three model explainers on aggregated MLP models, identified top contributing genes in different tissues that exhibited tissue-specific manifestation and were potential cancer biomarkers. In summary, our work provides novel insights and guidance for exploring biological mechanisms using explainable machine learning models.

Functional genomics uncovers the transcription factor BNC2 as required for myofibroblastic activation in fibrosis

Tissue injury triggers activation of mesenchymal lineage cells into wound-repairing myofibroblasts, whose unrestrained activity leads to fibrosis. Although this process is largely controlled at the transcriptional level, whether the main transcription factors involved have all been identified has remained elusive. Here, we report multi-omics analyses unraveling Basonuclin 2 (BNC2) as a myofibroblast identity transcription factor. Using liver fibrosis as a model for in-depth investigations, we first show that BNC2 expression is induced in both mouse and human fibrotic livers from different etiologies and decreases upon human liver fibrosis regression. Importantly, we found that BNC2 transcriptional induction is a specific feature of myofibroblastic activation in fibrotic tissues. Mechanistically, BNC2 expression and activities allow to integrate pro-fibrotic stimuli, including TGFβ and Hippo/YAP1 signaling, towards induction of matrisome genes such as those encoding type I collagen. As a consequence, Bnc2 deficiency blunts collagen deposition in livers of mice fed a fibrogenic diet. Additionally, our work establishes BNC2 as potentially druggable since we identified the thalidomide derivative CC-885 as a BNC2 inhibitor. Altogether, we propose that BNC2 is a transcription factor involved in canonical pathways driving myofibroblastic activation in fibrosis.

Myofibroblasts contribute to the development of liver fibrosis. Here, the authors report that the transcription factor Basonuclin 2 (BNC2) integrates fibrogenic signals and drives myofibroblastic transcriptional activation in liver fibrosis.

Identified Gefitinib Metabolism-Related lncRNAs can be Applied to Predict Prognosis, Tumor Microenvironment, and Drug Sensitivity in Non-Small Cell Lung Cancer

Gefitinib has shown promising efficacy in the treatment of patients with locally advanced or metastatic EGFR-mutated non-small cell lung cancer (NSCLC). Molecular biomarkers for gefitinib metabolism-related lncRNAs have not yet been elucidated. Here, we downloaded relevant genes and matched them to relevant lncRNAs. We then used univariate, LASSO, and multivariate regression to screen for significant genes to construct prognostic models. We investigated TME and drug sensitivity by risk score data. All lncRNAs with differential expression were selected for GO/KEGG analysis. Imvigor210 cohort was used to validate the value of the prognostic model. Finally, we performed a stemness indices difference analysis. lncRNA-constructed prognostic models were significant in the high-risk and low-risk subgroups. Immune pathways were identified in both groups at low risk. The higher the risk score the greater the value of exclusion, MDSC, and CAF. PRRophetic algorithm screened a total of 58 compounds. In conclusion, the prognostic model we constructed can accurately predict OS in NSCLC patients. Two groups of low-risk immune pathways are beneficial to patients. Gefitinib metabolism was again validated to be related to cytochrome P450 and lipid metabolism. Finally, drugs that might be used to treat NSCLC patients were screened.

Phase Separation: "The Master Key" to Deciphering the Physiological and Pathological Functions of Cells

Phase separation is a hot research field at present. It involves almost all aspects of cells and plays a significant role in cells, promising to be "a master key" in unlocking the mysteries of nature. In this review, the factors that affect phase separation are introduced, such as own component, electrostatic interaction, and chemical modification. Furthermore, the physiological roles of phase separation in cells, including molecules transport channel, gene expression and regulation, cellular division and differentiation, stress response, proteins refolding and degradation, cell connections, construction of skin barrier, and cell signals transmission, are highlighted. However, the disorder of phase separation leads to pathological condensates, which are associated with neurodegenerative diseases, tumors, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This relationship is considered the potential target for developing corresponded drugs and therapy. Some drugs targeting phase separation have improved meaningful, such as tankyrase, lipoamide, oligonucleotides, elvitagravir, nilotinib, CVL218, PJ34. All in all, mystery phase separation provides a new viewpoint for researchers to explore cells, and is expected to solve many unknown phenomena in nature.

Expression and Regulation Network of HDAC3 in Acute Myeloid Leukemia and the Implication for Targeted Therapy Based on Multidataset Data Mining

Background

Histone deacetylase 3 (HDAC3) plays an important role in the development and progression of a variety of cancers, but its regulatory mechanism in acute myeloid leukemia (LAML) is not entirely understood.

Methods

We analyzed the expression of HDAC3 in normal and cancerous tissues using Oncomine, UALCAN, and GEO databases. Changes of the HDAC3 gene were analyzed by cBioPortal. The genes coexpressed with HDAC3 were analyzed by WebGestalt, and the predicted signaling pathways in KEGG were discussed.

Results

We discovered that the expression of HDAC3 was elevated in some types of acute myeloid leukemia. The HDAC3 gene has a strong positive correlation with SLC25A5, NDUFA2, Cox4I1, and EIF3K, which regulate cell growth and development. HDAC3 transcription is higher in patients with FLT3 mutation than in healthy people. HDAC3 can be directly involved in regulating the thyroid hormone signaling pathway. MEF2D is directly involved in the cGMP-PKG signaling pathway, and the HDAC3 gene has a strong synergistic relationship with MEF2D. HDAC3 is indirectly involved in the cGMP-PKG signaling pathway, thereby indirectly regulating the expression levels of p53 and p21 genes in patients with LAML. Genomics of Drug Sensitivity in Cancer (GDSC) database analysis revealed that the application of the HDAC3 inhibitor can inhibit the proliferation of leukemia cells.

Conclusions

Therefore, our data suggest that HDAC3 may be a possible therapeutic target for acute myeloid leukemia.

Methylation-dependent and -independent roles of EZH2 synergize in CDCA8 activation in prostate cancer

Cell division cycle-associated 8 (CDCA8) is a component of chromosomal passenger complex (CPC) that participates in mitotic regulation. Although cancer-related CDCA8 hyperactivation has been widely observed, its molecular mechanism remains elusive. Here, we report that CDCA8 overexpression maintains tumorigenicity and is associated with poor clinical outcome in patients with prostate cancer (PCa). Notably, enhancer of zeste homolog 2 (EZH2) is identified to be responsible for CDCA8 activation in PCa. Genome-wide assays revealed that EZH2-induced H3K27 trimethylation represses let-7b expression and thus protects the let-7b-targeting CDCA8 transcripts. More importantly, EZH2 facilitates the self-activation of E2F1 by recruiting E2F1 to its own promoter region in a methylation-independent manner. The high level of E2F1 further promotes transcription of CDCA8 along with the other CPC subunits. Taken together, our study suggests that EZH2-mediated cell cycle regulation in PCa relies on both its methyltransferase and non-methyltransferase activities.

Combined inhibition of G9a and EZH2 suppresses tumor growth via synergistic induction of IL24-mediated apoptosis

G9a and EZH2 are two histone methyltransferases commonly upregulated in several cancer types, yet the precise roles that these enzymes play cooperatively in cancer is unclear. We demonstrate here that frequent concurrent upregulation of both G9a and EZH2 occurs in several human tumors. These methyltransferases cooperatively repressed molecular pathways responsible for tumor cell death. In genetically distinct tumor subtypes, concomitant inhibition of G9a and EZH2 potently induced tumor cell death, highlighting the existence of tumor cell survival dependency at the epigenetic level. G9a and EZH2 synergistically repressed expression of genes involved in the induction of endoplasmic reticulum (ER) stress and the production of reactive oxygen species. IL24 was essential for the induction of tumor cell death and was identified as a common target of G9a and EZH2. Loss-of-function of G9a and EZH2 activated the IL24-ER stress axis and increased apoptosis in cancer cells while not affecting normal cells. These results indicate that G9a and EZH2 promotes the evasion of ER stress-mediated apoptosis by repressing IL24 transcription, therefore suggesting that their inhibition may represent a potential therapeutic strategy for solid cancers.

Telomerase therapy reverses vascular senescence and extends lifespan in progeria mice

AIMS

Hutchinson-Gilford progeria syndrome (HGPS) is an accelerated ageing syndrome associated with premature vascular disease and death due to heart attack and stroke. In HGPS a mutation in lamin A (progerin) alters nuclear morphology and gene expression. Current therapy increases the lifespan of these children only modestly. Thus, greater understanding of the underlying mechanisms of HGPS is required to improve therapy. Endothelial cells (ECs) differentiated from induced pluripotent stem cells (iPSCs) derived from these patients exhibit hallmarks of senescence including replication arrest, increased expression of inflammatory markers, DNA damage, and telomere erosion. We hypothesized that correction of shortened telomeres may reverse these measures of vascular ageing.

METHODS AND RESULTS

We generated ECs from iPSCs belonging to children with HGPS and their unaffected parents. Telomerase mRNA (hTERT) was used to treat HGPS ECs. Endothelial morphology and functions were assessed, as well as proteomic and transcriptional profiles with attention to inflammatory markers, DNA damage, and EC identity genes. In a mouse model of HGPS, we assessed the effects of lentiviral transfection of mTERT on measures of senescence, focusing on the EC phenotype in various organs. hTERT treatment of human HGPS ECs improved replicative capacity; restored endothelial functions such as nitric oxide generation, acetylated low-density lipoprotein uptake and angiogenesis; and reduced the elaboration of inflammatory cytokines. In addition, hTERT treatment improved cellular and nuclear morphology, in association with a normalization of the transcriptional profile, effects that may be mediated in part by a reduction in progerin expression and an increase in sirtuin 1 (SIRT1). Progeria mice treated with mTERT lentivirus manifested similar improvements, with a reduction in inflammatory and DNA damage markers and increased SIRT1 in their vasculature and other organs. Furthermore, mTERT therapy increased the lifespan of HGPS mice.

CONCLUSION

Vascular rejuvenation using telomerase mRNA is a promising approach for progeria and other age-related diseases.

Prediction of Readmission in Geriatric Patients From Clinical Notes: Retrospective Text Mining Study

Background

Prior literature suggests that psychosocial factors adversely impact health and health care utilization outcomes. However, psychosocial factors are typically not captured by the structured data in electronic medical records (EMRs) but are rather recorded as free text in different types of clinical notes.

Objective

We here propose a text-mining approach to analyze EMRs to identify older adults with key psychosocial factors that predict adverse health care utilization outcomes, measured by 30-day readmission. The psychological factors were appended to the LACE (Length of stay, Acuity of the admission, Comorbidity of the patient, and Emergency department use) Index for Readmission to improve the prediction of readmission risk.

Methods

We performed a retrospective analysis using EMR notes of 43,216 hospitalization encounters in a hospital from January 1, 2017 to February 28, 2019. The mean age of the cohort was 67.51 years (SD 15.87), the mean length of stay was 5.57 days (SD 10.41), and the mean intensive care unit stay was 5% (SD 22%). We employed text-mining techniques to extract psychosocial topics that are representative of these patients and tested the utility of these topics in predicting 30-day hospital readmission beyond the predictive value of the LACE Index for Readmission.

Results

The added text-mined factors improved the area under the receiver operating characteristic curve of the readmission prediction by 8.46% for geriatric patients, 6.99% for the general hospital population, and 6.64% for frequent admitters. Medical social workers and case managers captured more of the psychosocial text topics than physicians.

Conclusions

The results of this study demonstrate the feasibility of extracting psychosocial factors from EMR clinical notes and the value of these notes in improving readmission risk prediction. Psychosocial profiles of patients can be curated and quantified from text mining clinical notes and these profiles can be successfully applied to artificial intelligence models to improve readmission risk prediction.

Epigenomic links from metabolism-methionine and chromatin architecture

Chromatin and associated epigenetic marks provide important platforms for gene regulation in response to metabolic changes associated with environmental exposures, including physiological stress, nutritional deprivation, and starvation. Numerous studies have shown that fluctuations of key metabolites can influence chromatin modifications, but their effects on chromatin structure (e.g. chromatin compaction, nucleosome arrangement, and chromatin loops) and how they appropriately deposit specific chemical modification on chromatin are largely unknown. Here, focusing on methionine metabolism, we discuss recent developments of metabolic effects on chromatin modifications and structure, as well as consequences on gene regulation.

An analysis of methylome evolution in primates

Although the investigation of the epigenome becomes increasingly important, still little is known about the long-term evolution of epigenetic marks and systematic investigation strategies are still lacking. Here, we systematically demonstrate the transfer of classic phylogenetic methods such as maximum likelihood based on substitution models, parsimony, and distance-based to interval-scaled epigenetic data. Using a great apes blood data set, we demonstrate that DNA methylation is evolutionarily conserved at the level of individual CpGs in promotors, enhancers, and genic regions. Our analysis also reveals that this epigenomic conservation is significantly correlated with its transcription factor binding density. Binding sites for transcription factors involved in neuron differentiation and components of AP-1 evolve at a significantly higher rate at methylation than at the nucleotide level. Moreover, our models suggest an accelerated epigenomic evolution at binding sites of BRCA1, chromobox homolog protein 2, and factors of the polycomb repressor 2 complex in humans. For most genomic regions, the methylation-based reconstruction of phylogenetic trees is at par with sequence-based reconstruction. Most strikingly, phylogenetic reconstruction using methylation rates in enhancer regions was ineffective independently of the chosen model. We identify a set of phylogenetically uninformative CpG sites enriched in enhancers controlling immune-related genes.

New Directions in Therapeutic Angiogenesis and Arteriogenesis in Peripheral Arterial Disease

The prevalence of peripheral arterial disease (PAD) in the United States exceeds 10 million people, and PAD is a significant cause of morbidity and mortality across the globe. PAD is typically caused by atherosclerotic obstructions in the large arteries to the leg(s). The most common clinical consequences of PAD include pain on walking (claudication), impaired functional capacity, pain at rest, and loss of tissue integrity in the distal limbs that may lead to lower extremity amputation. Patients with PAD also have higher than expected rates of myocardial infarction, stroke, and cardiovascular death. Despite advances in surgical and endovascular procedures, revascularization procedures may be suboptimal in relieving symptoms, and some patients with PAD cannot be treated because of comorbid conditions. In some cases, relieving obstructive disease in the large conduit arteries does not assure complete limb salvage because of severe microvascular disease. Despite several decades of investigational efforts, medical therapies to improve perfusion to the distal limb are of limited benefit. Whereas recent studies of anticoagulant (eg, rivaroxaban) and intensive lipid lowering (such as PCSK9 [proprotein convertase subtilisin/kexin type 9] inhibitors) have reduced major cardiovascular and limb events in PAD populations, chronic ischemia of the limb remains largely resistant to medical therapy. Experimental approaches to improve limb outcomes have included the administration of angiogenic cytokines (either as recombinant protein or as gene therapy) as well as cell therapy. Although early angiogenesis and cell therapy studies were promising, these studies lacked sufficient control groups and larger randomized clinical trials have yet to achieve significant benefit. This review will focus on what has been learned to advance medical revascularization for PAD and how that information might lead to novel approaches for therapeutic angiogenesis and arteriogenesis for PAD.

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare

Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as many of its signs and symptoms are similar to other less critical conditions. We develop an artificial intelligence algorithm, SERA algorithm, which uses both structured data and unstructured clinical notes to predict and diagnose sepsis. We test this algorithm with independent, clinical notes and achieve high predictive accuracy 12 hours before the onset of sepsis (AUC 0.94, sensitivity 0.87 and specificity 0.87). We compare the SERA algorithm against physician predictions and show the algorithm's potential to increase the early detection of sepsis by up to 32% and reduce false positives by up to 17%. Mining unstructured clinical notes is shown to improve the algorithm's accuracy compared to using only clinical measures for early warning 12 to 48 hours before the onset of sepsis.

Control of Cell Identity by the Nuclear Receptor HNF4 in Organ Pathophysiology

Hepatocyte Nuclear Factor 4 (HNF4) is a transcription factor (TF) belonging to the nuclear receptor family whose expression and activities are restricted to a limited number of organs including the liver and gastrointestinal tract. In this review, we present robust evidence pointing to HNF4 as a master regulator of cellular differentiation during development and a safekeeper of acquired cell identity in adult organs. Importantly, we discuss that transient loss of HNF4 may represent a protective mechanism upon acute organ injury, while prolonged impairment of HNF4 activities could contribute to organ dysfunction. In this context, we describe in detail mechanisms involved in the pathophysiological control of cell identity by HNF4, including how HNF4 works as part of cell-specific TF networks and how its expression/activities are disrupted in injured organs.