Cancer metabolism and dietary interventions

Metabolic remodeling is a key feature of cancer development.Knowledge of cancer metabolism has greatly expanded since the first observation of abnormal metabolism in cancer cells,the so-called Warburg effect.Malignant cells tend to modify cellular metabolism to favor specialized fermentation over the aerobic respiration usually used by most normal cells.Thus,targeted cancer therapies based on reprogramming nutrient or metabolite metabolism have received substantial attention both conceptually and in clinical practice.In particular,the management of nutrient availability is becoming more attractive in cancer treatment.In this review,we discuss recent findings on tumor metabolism and potential dietary interventions based on the specific characteristics of tumor metabolism.First,we present a comprehensive overview of changes in macronutrient metabolism.Carbohydrates,amino acids,and lipids,are rewired in the cancer microenvironment individually or systematically.Second,we summarize recent progress in cancer interventions applying different types of diets and specific nutrient restrictions in pre-clinical research or clinical trials.

A single-cell morpho-transcriptomic map of brassinosteroid action in the Arabidopsis root

The effects of brassinosteroid signaling on shoot and root development have been characterized in great detail but a simple consistent positive or negative impact on a basic cellular parameter was not identified.In this study,we combined digital 3D single-cell shape analysis and single-cell mRNA sequencing to charac-terize root meristems and mature root segments of brassinosteroid-blind mutants and wild type.The resul-tant datasets demonstrate that brassinosteroid signaling affects neither cell volume nor cell proliferation capacity.Instead,brassinosteroid signaling is essential for the precise orientation of cell division planes and the extent and timing of anisotropic cell expansion.Moreover,we found that the cell-aligning effects of brassinosteroid signaling can propagate to normalize the anatomy of both adjacent and distant brassinosteroid-blind cells through non-cell-autonomous functions,which are sufficient to restore growth vigor.Finally,single-cell transcriptome data discern directly brassinosteroid-responsive genes from genes that can react non-cell-autonomously and highlight arabinogalactans as sentinels of brassinosteroid-dependent anisotropic cell expansion.

Predictive Model and Risk Factors for Case Fatality of COVID-19: A Cohort of 21,392 Cases in Hubei, China

An increasing number of patients are being killed by coronavirus disease 2019(COVID-19),however,risk factors for the fatality of COVID-19 remain unclear.A total of 21,392 COVID-19 cases were recruited in the Hubei Province of China between December 2019 and February 2020,and followed up until March 18,2020.We adopted Cox regression models to investigate the risk factors for case fatality and predicted the death probability under specific combinations of key predictors.Among the 21,392 patients,1,020(4.77%)died of COVID-19.Multivariable analyses showed that factors,including age(R60 versus<45 years,hazard ratio[HR]=7.32;95%confidence interval[CI],5.42,9.89),sex(male versus female,HR=1.31;95%CI,1.15,1.50),severity of the disease(critical versus mild,HR=39.98;95%CI,29.52,48.86),comorbidity(HR=1.40;95%CI,1.23,1.60),highest body temperature(>39
C versus<39
C,HR=1.28;95%CI,1.09,1.49),white blood cell counts(>103109/L versus(4–10)3109/L,HR=1.69;95%CI,1.35,2.13),and lymphocyte counts(<0.83109/L versus(0.8–4)3109/L,HR=1.26;95%CI,1.06,1.50)were significantly associated with case fatality of COVID-19 patients.Individuals of an older age,who were male,with comorbidities,and had a critical illness had the highest death probability,with 21%,36%,46%,and 54%within 1–4 weeks after the symptom onset.Risk factors,including demographic characteristics,clinical symptoms,and laboratory factors were confirmed to be important determinants of fatality of COVID-19.Our predictive model can provide scientific evidence for a more rational,evidence-driven allocation of scarce medical resources to reduce the fatality of COVID-19.