Rui Liu

Comprehensive identification and cataloging of copy number variations (CNVs) is required to provide a complete view of human genetic variation. The resolution of CNV detection in previous experimental designs has been limited to tens or hundreds of kilobases. Here we present PennCNV, a hidden Markov model (HMM) based approach, for kilobase-resolution detection of CNVs from Illumina high-density SNP genotyping data. This algorithm incorporates multiple sources of information, including total signal intensity and allelic intensity ratio at each SNP marker, the distance between neighboring SNPs, the allele frequency of SNPs, and the pedigree information where available. We applied PennCNV to genotyping data generated for 112 HapMap individuals; on average, we detected approximately 27 CNVs for each individual with a median size of approximately 12 kb. Excluding common rearrangements in lymphoblastoid cell lines, the fraction of CNVs in offspring not detected in parents (CNV-NDPs) was 3.3%. Our results demonstrate the feasibility of whole-genome fine-mapping of CNVs via high-density SNP genotyping.

BACKGROUND: Ferroptosis is a novel mode of non-apoptotic cell death induced by build-up of toxic lipid peroxides (lipid-ROS) in an iron dependent manner. Cancer-associated fibroblasts (CAFs) support tumor progression and drug resistance by secreting various bioactive substances, including exosomes. Yet, the role of CAFs in regulating lipid metabolism as well as ferroptosis of cancer cells is still unexplored and remains enigmatic. METHODS: Ferroptosis-related genes in gastric cancer (GC) were screened by using mass spectrum; exosomes were isolated by ultra-centrifugation and CAF secreted miRNAs were determined by RT-qPCR. Erastin was used to induce ferroptosis, and ferroptosis levels were evaluated by measuring lipid-ROS, cell viability and mitochondrial membrane potential. RESULTS: Here, we provide clinical evidence to show that arachidonate lipoxygenase 15 (ALOX15) is closely related with lipid-ROS production in gastric cancer, and that exosome-miR-522 serves as a potential inhibitor of ALOX15. By using primary stromal cells and cancer cells, we prove that exosome-miR-522 is mainly derived from CAFs in tumor microenvironment. Moreover, heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) was found to mediate miR-522 packing into exosomes, and ubiquitin-specific protease 7 (USP7) stabilizes hnRNPA1 through de-ubiquitination. Importantly, cisplatin and paclitaxel promote miR-522 secretion from CAFs by activating USP7/hnRNPA1 axis, leading to ALOX15 suppression and decreased lipid-ROS accumulation in cancer cells, and ultimately result in decreased chemo-sensitivity. CONCLUSIONS: The present study demonstrates that CAFs secrete exosomal miR-522 to inhibit ferroptosis in cancer cells by targeting ALOX15 and blocking lipid-ROS accumulation. The intercellular pathway, comprising USP7, hnRNPA1, exo-miR-522 and ALOX15, reveals new mechanism of acquired chemo-resistance in GC.

Considerable evidence suggests that during the progression of complex diseases, the deteriorations are not necessarily smooth but are abrupt, and may cause a critical transition from one state to another at a tipping point. Here, we develop a model-free method to detect early-warning signals of such critical transitions, even with only a small number of samples. Specifically, we theoretically derive an index based on a dynamical network biomarker (DNB) that serves as a general early-warning signal indicating an imminent bifurcation or sudden deterioration before the critical transition occurs. Based on theoretical analyses, we show that predicting a sudden transition from small samples is achievable provided that there are a large number of measurements for each sample, e.g., high-throughput data. We employ microarray data of three diseases to demonstrate the effectiveness of our method. The relevance of DNBs with the diseases was also validated by related experimental data and functional analysis.

BACKGROUND: Developing effective strategies for signaling the pre-disease state of complex diseases, a state with high susceptibility before the disease onset or deterioration, is urgently needed because such state usually followed by a catastrophic transition into a worse stage of disease. However, it is a challenging task to identify such pre-disease state or tipping point in clinics, where only one single sample is available and thus results in the failure of most statistic approaches. METHODS: In this study, we presented a single-sample-based computational method to detect the early-warning signal of critical transition during the progression of complex diseases. Specifically, given a set of reference samples which were regarded as background, a novel index called single-sample Kullback-Leibler divergence (sKLD), was proposed to explore and quantify the disturbance on the background caused by a case sample. The pre-disease state is then signaled by the significant change of sKLD. RESULTS: The novel algorithm was developed and applied to both numerical simulation and real datasets, including lung squamous cell carcinoma, lung adenocarcinoma, stomach adenocarcinoma, thyroid carcinoma, colon adenocarcinoma, and acute lung injury. The successful identification of pre-disease states and the corresponding dynamical network biomarkers for all six datasets validated the effectiveness and accuracy of our method. CONCLUSIONS: The proposed method effectively explores and quantifies the disturbance on the background caused by a case sample, and thus characterizes the criticality of a biological system. Our method not only identifies the critical state or tipping point at a single sample level, but also provides the sKLD-signaling markers for further practical application. It is therefore of great potential in personalized pre-disease diagnosis.