A combined risk model for the multi-encompassing identification of heterogeneities of prognoses, biological pathway variations and immune states for sepsis patients

Background Sepsis is a highly heterogeneous syndrome with stratified severity levels and immune states. Even in patients with similar clinical appearances, the underlying signal transduction pathways are significantly different. To identify the heterogeneities of sepsis from multiple angles, we aimed to establish a combined risk model including the molecular risk score for rapid mortality prediction, pathway risk score for the identification of biological pathway variations, and immunity risk score for guidance with immune-modulation therapy. Methods We systematically searched and screened the mRNA expression profiles of patients with sepsis in the Gene Expression Omnibus public database. The screened datasets were divided into a training cohort and a validation cohort. In the training cohort, authentic prognostic predictor characteristics (differentially expressed mRNAs, pathway activity variations and immune cells) were screened for model construction through bioinformatics analysis and univariate Cox regression, and a P value less than 0.05 of univariate Cox regression on 28-day mortality was set as the cut-off value. The combined risk model was finally established by the decision tree algorithm. In the validation cohort, the model performance was assessed and validated by C statistics and the area under the receiver operating characteristic curve (AUC). Additionally, the current models were further compared in clinical value with traditional indicators, including procalcitonin (PCT) and interleukin-8 (IL-8). Results Datasets from two sepsis cohort studies with a total of 585 consecutive sepsis patients admitted to two intensive care units were downloaded as the training cohort (n = 479) and external validation cohort (n = 106). In the training cohort, 15 molecules, 20 pathways and 4 immune cells were eventually enrolled in model construction. These prognostic factors mainly reflected hypoxia, cellular injury, metabolic disorders and immune dysregulation in sepsis patients. In the validation cohort, the AUCs of the molecular model, pathway model, immune model, and combined model were 0.81, 0.82, 0.62 and 0.873, respectively. The AUCs of the traditional biomarkers (PCT and IL-8) were 0.565 and 0.585, respectively. The survival analysis indicated that patients in the high-risk group identified by models in the current study had a poor prognosis (P < 0.05). The above results indicated that the models in this study are all superior to the traditional biomarkers for the predicting the prognosis of sepsis patients. Furthermore, the current study provides some therapeutic recommendations for patients with high risk scores identified by the three submodels. Conclusions In summary, the present study provides opportunities for bedside tests that could quantitatively and rapidly measure heterogeneous prognosis, underlying biological pathway variations and immune dysfunction in sepsis patients. Further therapeutic recommendations for patients with high risk scores could improve the therapeutic system for sepsis.

Higher order genetic interactions switch cancer genes from two-hit to one-hit drivers

The classic two-hit model posits that both alleles of a tumor suppressor gene (TSG) must be inactivated to cause cancer. In contrast, for some oncogenes and haploinsufficient TSGs, a single genetic alteration can suffice to increase tumor fitness. Here, by quantifying the interactions between mutations and copy number alterations (CNAs) across 10,000 tumors, we show that many cancer genes actually switch between acting as one-hit or two-hit drivers. Third order genetic interactions identify the causes of some of these switches in dominance and dosage sensitivity as mutations in other genes in the same biological pathway. The correct genetic model for a gene thus depends on the other mutations in a genome, with a second hit in the same gene or an alteration in a different gene in the same pathway sometimes representing alternative evolutionary paths to cancer.

Biological Pathway Introduction of Quarantine Animal Disease through International Waste at Soekarno Hatta International Airport, Indonesia

International flights coming to Indonesia, in addition to bringing in passengers, also brings in passenger’s food waste and garbage from the activities in the airplane. Soekarno Hatta International Airport (SHIA), as one of the busiest airports in Indonesia has great potential as a waste producer. International waste has a higher risk in terms of spreading disease to the environment around the airport, which will ultimately impact human and animal health in general. Waste risk assessment carried by international aircraft as a risk carrier of quarantine animal disease entering Indonesia has never been done. The aim of this study was to identify the biological pathway from the entry of pathogens through international waste and how to prevent it. The results, there were three pathway of international waste management at SHIA. The first pathway, the waste was not unloaded from international aircraft and returned to the country of origin. The second pathway, international waste was unloaded from international aircraft, then destroyed in incinerators inside the airport area. The third pathway, the waste was managed by the inflight catering company and taken out of the SHIA. The third pathway has a highest risk as an entry pathway of quarantine animal disease. There was possibility that food waste was reused as animal feed. Regulations regarding airport waste management is exist, but the implementation is not optimal. The authority and agencies related to international waste at SHIA need to enforce the existing rules about international waste management, to prevent the spread of diseases due to waste.

Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning

Breast carcinoma is the most common cancer among women worldwide that consists of a heterogeneous group of subtype diseases. The whole-slide images (WSIs) can capture the cell-level heterogeneity, and are routinely used for cancer diagnosis by pathologists. However, key driver genetic mutations related to targeted therapies are identified by genomic analysis like high-throughput molecular profiling. In this study, we develop a deep-learning model to predict the genetic mutations and biological pathway activities directly from WSIs. Our study offers unique insights into WSI visual interactions between mutation and its related pathway, enabling a head-to-head comparison to reinforce our major findings. Using the histopathology images from the Genomic Data Commons Database, our model can predict the point mutations of six important genes (AUC 0.68–0.85) and copy number alteration of another six genes (AUC 0.69–0.79). Additionally, the trained models can predict the activities of three out of ten canonical pathways (AUC 0.65–0.79). Next, we visualized the weight maps of tumor tiles in WSI to understand the decision-making process of deep-learning models via a self-attention mechanism. We further validated our models on liver and lung cancers that are related to metastatic breast cancer. Our results provide insights into the association between pathological image features, molecular outcomes, and targeted therapies for breast cancer patients.

Paraquat-induced cholesterol biosynthesis proteins dysregulation in human brain microvascular endothelial cells

Despite Paraquat (PQ) being banned in several countries, it is still one of the most commonly used herbicides in agriculture. This compound is known to induce damaging effects on human and animal brain cells by generating Reactive Oxygen Species (ROS). However, there is few evidence of PQ effect on Human Brain Microvascular Endothelial Cells (HBMECs), one of the major component of the Blood–Brain Barrier (BBB). The present study aimed at unraveling biological mechanisms associated to the exposure of 1, 10 and 100 µM of PQ for 24 h on HBMECs. High-throughput mass spectrometry-based proteomics using data-independent acquisition (DIA) was applied. Biological pathway enrichment and cellular assays such as mitochondrial respiration and cholesterol level were performed to verify proteomics results. A total of 3753 proteins were quantified out of which 419 were significantly modulated by paraquat exposure. Biological pathway enrichment revealed the ubiquinone metabolism, a pathway directly linked to mitochondrial complex I proteins, confirming the well-known mechanism of PQ inducing oxidative stress. Additionally, this study also described the cholesterol biosynthesis modulation on HBMECs not yet described. In conclusion, our data indicate the toxic effect of PQ on HBMECs by downregulating proteins involved in mitochondrial complex I and cholesterol pathways.