Susumu Ohno proposed in 1967 that, during the origin of mammalian sex chromosomes from a pair of autosomes, per-allele expression levels of X-linked genes were doubled to compensate for the degeneration of their Y homologs. This conjecture forms the foundation of the current evolutionary model of sex chromosome dosage compensation, but has been tested in mammals only indirectly via a comparison of expression levels between X-linked and autosomal genes in the same genome. The test results have been controversial, because examinations of different gene sets led to different conclusions that either support or refute Ohno's hypothesis. Here we resolve this uncertainty by directly comparing mammalian X-linked genes with their one-to-one orthologs in species that diverged before the origin of the mammalian sex chromosomes. Analyses of RNA sequencing data and proteomic data provide unambiguous evidence for expression halving (i.e., no change in per-allele expression level) of X-linked genes during evolution, with the exception of only ∼5% of genes that encode members of large protein complexes. We conclude that Ohno's hypothesis is rejected for the vast majority of genes, reopening the search for the evolutionary force driving the origin of chromosomewide X inactivation in female mammals.

Theoretical reasoning suggests that cancer may result from a knockdown of the genetic constraints that evolved for the maintenance of metazoan multicellularity. By characterizing the whole-life history of a xenograft tumour, here we show that metastasis is driven by positive selection for general loss-of-function mutations on multicellularity-related genes. Expression analyses reveal mainly downregulation of multicellularity-related genes and an evolving expression profile towards that of embryonic stem cells, the cell type resembling unicellular life in its capacity of unlimited clonal proliferation. Also, the emergence of metazoan multicellularity B600 Myr ago is accompanied by an elevated birth rate of cancer genes, and there are more loss-of-function tumour suppressors than activated oncogenes in a typical tumour. These data collectively suggest that cancer represents a loss-of-functiondriven reverse evolution back to the unicellular 'ground state'. This cancer evolution model may account for inter-/intratumoural genetic heterogeneity, could explain distant-organ metastases and hold implications for cancer therapy.

It is unknown how the composition and structure of DNA within the cell affect spontaneous mutations. Theory suggests that in eukaryotic genomes, nucleosomal DNA undergoes fewer C→T mutations because of suppressed cytosine hydrolytic deamination relative to nucleosome-depleted DNA. Comparative genomic analyses and a mutation accumulation experiment showed that nucleosome occupancy nearly eliminated cytosine deamination, resulting in an ~50% decrease of the C→T mutation rate in nucleosomal DNA. Furthermore, the rates of G→T and A→T mutations were also about twofold suppressed by nucleosomes. On the basis of these results, we conclude that nucleosome-dependent mutation spectra affect eukaryotic genome structure and evolution and may have implications for understanding the origin of mutations in cancers and in induced pluripotent stem cells.

The current pandemic of coronavirus disease 19 (COVID-19) has affected more than 160 million of individuals and caused millions of deaths worldwide at least in part due to the unclarified pathophysiology of this disease. Therefore, identifying the underlying molecular mechanisms of COVID-19 is critical to overcome this pandemic. Metabolites mirror the disease progression of an individual by acquiring extensive insights into the pathophysiological significance during disease progression. We provide a comprehensive view of metabolic characterization of sera from COVID-19 patients at all stages using untargeted and targeted metabolomic analysis. As compared with the healthy controls, we observed different alteration patterns of circulating metabolites from the mild, severe and recovery stages, in both discovery cohort and validation cohort, which suggest that metabolic reprogramming of glucose metabolism and urea cycle are potential pathological mechanisms for COVID-19 progression. Our findings suggest that targeting glucose metabolism and urea cycle may be a viable approach to fight against COVID-19 at various stages along the disease course.

The prediction of relapse in childhood acute lymphoblastic leukemia (ALL) is a critical factor for successful treatment and follow-up planning. Our goal was to construct an ALL relapse prediction model based on machine learning algorithms. Monte Carlo cross-validation nested by 10-fold crossvalidation was used to rank clinical variables on the randomly split training sets of 336 newly diagnosed ALL children, and a forward feature selection algorithm was employed to find the shortest list of most discriminatory variables. To enable an unbiased estimation of the prediction model to new patients, besides the split test sets of 150 patients, we introduced another independent data set of 84 patients to evaluate the model. The Random Forest model with 14 features achieved a cross-validation accuracy of 0.827 ± 0.031 on one set and an accuracy of 0.798 on the other, with the area under the curve of 0.902 ± 0.027 and 0.904, respectively. The model performed well across different risk-level groups, with the best accuracy of 0.829 in the standard-risk group. To our knowledge, this is the first study to use machine learning models to predict childhood ALL relapse based on medical data from Electronic Medical Record, which will further facilitate stratification treatments.Acute lymphoblastic leukemia (ALL) is the most common malignant cancer among children 1 . Current risk-adapted treatments and supportive care have increased the survival rate to over 90% in the developed countries 2, 3 . However, approximately 20% of children who relapse have a poor prognosis, making ALL the leading cause of cancer mortality in pediatric disorders 4 . A major challenge in childhood ALL management is to classify patients into appropriate risk groups for better management. Stratifying chemotherapeutic treatment through the early recognition of relevant outcomes is critically important in order to mitigate poor disease courses in these patients 5 .Previous group-level studies have identified many potential prognostic factors for childhood ALL, such as white blood cell (WBC) counts, age at diagnosis, response to prednisone and some gene fusions like BCR-ABL, TEL-AML1 and E2A-PBX1. Moreover, immunophenotype (T cell or B cell), percentage of lymphoblast in bone marrow (BM) on day 15 and day 33, level of minimal residual disease (MRD) may also help to identify the probability of relapse risk for patients at early therapy 3,6,7 . However, despite insight into various prognostic features, there is no clear consensus regarding how and which of these features should be combined for prediction. Clinicians still lack accurate tools to estimate a patient's risk of ALL relapse in the early course of treatment.Machine learning is a data-driven analytic approach that specializes in the integration of multiple risk factors into a predictive tool 8 . The application of different techniques for feature selection and classification in multidimensional heterogeneous data can provide promising tools for inference in medicine. Over the past several decades, such ...