BACH1 recruits NANOG and histone H3 lysine 4 methyltransferase MLL/SET1 complexes to regulate enhancer-promoter activity and maintains pluripotency.

Maintenance of stem-cell identity requires proper regulation of enhancer activity. Both transcription factors OCT4/SOX2/NANOG and histone methyltransferase complexes MLL/SET1 were shown to regulate enhancer activity, but how they are regulated in embryonic stem cells (ESCs) remains further studies. Here, we report a transcription factor BACH1, which directly interacts with OCT4/SOX2/NANOG (OSN) and MLL/SET1 methyltransferase complexes and maintains pluripotency in mouse ESCs (mESCs). BTB domain and bZIP domain of BACH1 are required for these interactions and pluripotency maintenance. Loss of BACH1 reduced the interaction between NANOG and MLL1/SET1 complexes, and decreased their occupancy on chromatin, and further decreased H3 lysine 4 trimethylation (H3K4me3) level on gene promoters and (super-) enhancers, leading to decreased enhancer activity and transcription activity, especially on stemness-related genes. Moreover, BACH1 recruited NANOG through chromatin looping and regulated remote NANOG binding, fine-tuning enhancer-promoter activity and gene expression. Collectively, these observations suggest that BACH1 maintains pluripotency in ESCs by recruiting NANOG and MLL/SET1 complexes to chromatin and maintaining the trimethylated state of H3K4 and enhancer-promoter activity, especially on stemness-related genes.

Systems biology in aging: linking the old and the young.

Aging can be defined as a process of progressive decline in the physiological capacity of an organism, manifested by accumulated alteration and destabilization at the whole system level. Systems biology approaches offer a promising new perspective to examine the old problem of aging. We begin this review by introducing the concepts of systems biology, and then illustrate the application of systems biology approaches to aging research, from gene expression profiling to network analysis. We then introduce the network that can be constructed using known lifespan and aging regulators, and conclude with a look forward to the future of systems biology in aging research. In summary, systems biology is not only a young field that may help us understand aging at a higher level, but also an important platform that can link different levels of knowledge on aging, moving us closer to a more comprehensive control of systematic decline during aging.

Individual variation of the SARS-CoV-2 receptor ACE2 gene expression and regulation.

The COVID-19 coronavirus is now spreading worldwide. Its pathogen, SARS-CoV-2, has been shown to use angiotensin-converting enzyme 2 (ACE2) as its host cell receptor, same as the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2003. Epidemiology studies found males although only slightly more likely to be infected than females account for the majority of the severely ill and fatality, which also bias for people older than 60 years or with metabolic and cardiovascular diseases. Here by analyzing GTEx and other public data in 30 tissues across thousands of individuals, we found a significantly higher level in Asian females, an age-dependent decrease in all ethnic groups, and a highly significant decrease in type II diabetic patients of ACE2 expression. Consistently, the most significant expression quantitative loci (eQTLs) contributing to high ACE2 expression are close to 100% in East Asians, >30% higher than other ethnic groups. A shockingly common enrichment of viral infection pathways was found among ACE2 anti-expressed genes, and multiple binding sites of virus infection related transcription factors and sex hormone receptors locate at ACE2 regulatory regions. Human and mice data analysis further revealed ACE2 expression is reduced in T2D patients and with inflammatory cytokine treatment and upregulated by estrogen and androgen (both decrease with age). Our findings revealed a negative correlation between ACE2 expression and COVID-19 fatality at both population and molecular levels. These results will be instrumental when designing potential prevention and treatment strategies for ACE2 binding coronaviruses in general.

Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle.

Not all individuals age at the same rate. Methods such as the 'methylation clock' are invasive, rely on expensive assays of tissue samples and infer the ageing rate by training on chronological age, which is used as a reference for prediction errors. Here, we develop models based on convoluted neural networks through training on non-invasive three-dimensional (3D) facial images of approximately 5,000 Han Chinese individuals that achieve an average difference between chronological or perceived age and predicted age of ±2.8 and 2.9 yr, respectively. We further profile blood transcriptomes from 280 individuals and infer the molecular regulators mediating the impact of lifestyle on the facial-ageing rate through a causal-inference model. These relationships have been deposited and visualized in the Human Blood Gene Expression-3D Facial Image (HuB-Fi) database. Overall, we find that humans age at different rates both in the blood and in the face, but do so coherently and with heterogeneity peaking at middle age. Our study provides an example of how artificial intelligence can be leveraged to determine the perceived age of humans as a marker of biological age, while no longer relying on prediction errors of chronological age, and to estimate the heterogeneity of ageing rates within a population.