Sepsis was first described by the ancient Greek physicians over 2000 years ago. The pathophysiology of the disease, however, is still not fully understood and hence the mortality rate is still unacceptably high due to lack of specific therapies. In the last decade, great progress has been made by shifting the focus of research from systemic inflammatory response syndrome (SIRS) to multiple organ dysfunction syndrome (MODS). Sepsis has been re-defined as infection-induced MODS in 2016. How infection leads to MODS is not clear, but what mediates MODS becomes the major topic in understanding the molecular mechanisms and developing specific therapies. Recently, the mechanism of infection-induced extensive immune cell death which releases a large quantity of damage-associated molecular patterns (DAMPs) and their roles in the development of MODS as well as immunosuppression during sepsis have attracted much attention. Growing evidence supports the hypothesis that DAMPs, including high-mobility group box 1 protein (HMGB1), cell-free DNA (cfDNA) and histones as well as neutrophil extracellular traps (NETs), may directly or indirectly contribute significantly to the development of MODS. Here, we provide an overview of the mechanisms and consequences of infection-induced extensive immune cell death during the development of sepsis. We also propose a pivotal pathway from a local infection to eventual sepsis and a potential combined therapeutic strategy for targeting sepsis.

Summary: Background: COVID-19 pandemic has developed rapidly and the ability to stratify the most vulnerable patients is vital. However, routinely used severity scoring systems are often low on diagnosis, even in non-survivors. Therefore, clinical prediction models for mortality are urgently required. Methods: We developed and internally validated a multivariable logistic regression model to predict inpatient mortality in COVID-19 positive patients using data collected retrospectively from Tongji Hospital, Wuhan (299 patients). External validation was conducted using a retrospective cohort from Jinyintan Hospital, Wuhan (145 patients). Nine variables commonly measured in these acute settings were considered for model development, including age, biomarkers and comorbidities. Backwards stepwise selection and bootstrap resampling were used for model development and internal validation. We assessed discrimination via the C statistic, and calibration using calibration-in-the-large, calibration slopes and plots. Findings: The final model included age, lymphocyte count, lactate dehydrogenase and SpO ₂ as independent predictors of mortality. Discrimination of the model was excellent in both internal (c=0·89) and external (c=0·98) validation. Internal calibration was excellent (calibration slope=1). External validation showed some over-prediction of risk in low-risk individuals and under-prediction of risk in high-risk individuals prior to recalibration. Recalibration of the intercept and slope led to excellent performance of the model in independent data. Interpretation: COVID-19 is a new disease and behaves differently from common critical illnesses. This study provides a new prediction model to identify patients with lethal COVID-19. Its practical reliance on commonly available parameters should improve usage of limited healthcare resources and patient survival rate. Funding: This study was supported by following funding: Key Research and Development Plan of Jiangsu Province (BE2018743 and BE2019749), National Institute for Health Research (NIHR) (PDF-2018-11-ST2-006), British Heart Foundation (BHF) (PG/16/65/32313) and Liverpool University Hospitals NHS Foundation Trust in UK. Research in context: Evidence before this study: Since the outbreak of COVID-19, there has been a pressing need for development of a prognostic tool that is easy for clinicians to use. Recently, a Lancet publication showed that in a cohort of 191 patients with COVID-19, age, SOFA score and D-dimer measurements were associated with mortality. No other publication involving prognostic factors or models has been identified to date. Added value of this study: In our cohorts of 444 patients from two hospitals, SOFA scores were low in the majority of patients on admission. The relevance of D-dimer could not be verified, as it is not included in routine laboratory tests. In this study, we have established a multivariable clinical prediction model using a development cohort of 299 patients from one hospital. After backwards selection, four variables, including age, lymphocyte count, lactate dehydrogenase and SpO ₂ remained in the model to predict mortality. This has been validated internally and externally with a cohort of 145 patients from a different hospital. Discrimination of the model was excellent in both internal (c=0·89) and external (c=0·98) validation. Calibration plots showed excellent agreement between predicted and observed probabilities of mortality after recalibration of the model to account for underlying differences in the risk profile of the datasets. This demonstrated that the model is able to make reliable predictions in patients from different hospitals. In addition, these variables agree with pathological mechanisms and the model is easy to use in all types of clinical settings. Implication of all the available evidence: After further external validation in different countries the model will enable better risk stratification and more targeted management of patients with COVID-19. With the nomogram, this model that is based on readily available parameters can help clinicians to stratify COVID-19 patients on diagnosis to use limited healthcare resources effectively and improve patient outcome.

Complement activation leads to membrane attack complex formation, which can lyse not only pathogens but also host cells. Histones can be released from the lysed or damaged cells and serve as a major type of damage-associated molecular pattern, but their effects on the complement system are not clear. In this study, we pulled down two major proteins from human serum using histone-conjugated beads: one was C-reactive protein and the other was C4, as identified by mass spectrometry. In surface plasmon resonance analysis, histone H3 and H4 showed stronger binding to C4 than other histones, with K_D around 1 nM. The interaction did not affect C4 cleavage to C4a and C4b. Because histones bind to C4b, a component of C3 and C5 convertases, their activities were significantly inhibited in the presence of histones. Although it is not clear whether the inhibition was achieved through blocking C3 and C5 convertase assembly or just through reducing their activity, the outcome was that both classical and mannose-binding lectin pathways were dramatically inhibited. Using a high concentration of C4 protein, histone-suppressed complement activity could not be fully restored, indicating C4 is not the only target of histones in those pathways. In contrast, the alternative pathway was almost spared, but the overall complement activity activated by zymosan was inhibited by histones. Therefore, we believe that histones inhibiting complement activation is a natural feedback mechanism to prevent the excessive injury of host cells.

Most patients who die of cancer do so from its metastasis to other organs. The calcium-binding protein S100A4 can induce cell migration/invasion and metastasis in experimental animals and is overexpressed in most human metastatic cancers. Here, we report that a novel inhibitor of S100A4 can specifically block its increase in cell migration in rat (IC₅₀, 46 µM) and human (56 µM) triple negative breast cancer (TNBC) cells without affecting Western-blotted levels of S100A4. The moderately-weak S100A4-inhibitory compound, US-10113 has been chemically attached to thalidomide to stimulate the proteasomal machinery of a cell. This proteolysis targeting chimera (PROTAC) RGC specifically eliminates S100A4 in the rat (IC₅₀, 8 nM) and human TNBC (IC₅₀, 3.2 nM) cell lines with a near 20,000-fold increase in efficiency over US-10113 at inhibiting cell migration (IC₅₀, 1.6 nM and 3.5 nM, respectively). Knockdown of S100A4 in human TNBC cells abolishes this effect. When PROTAC RGC is injected with mouse TNBC cells into syngeneic Balb/c mice, the incidence of experimental lung metastases or local primary tumour invasion and spontaneous lung metastasis is reduced in the 10–100 nM concentration range (Fisher’s Exact test, p ≤ 0.024). In conclusion, we have established proof of principle that destructive targeting of S100A4 provides the first realistic chemotherapeutic approach to selectively inhibiting metastasis.