Modern wheat production comes from two polyploid species, Triticum aestivum and Triticum turgidum (var durum), which putatively arose from diploid ancestors Triticum urartu, Aegilops speltoides, and Aegilops tauschii. How gene expression during embryogenesis and grain development in wheats has been shaped by the differing contributions of diploid genomes through hybridization, polyploidization, and breeding selection is not well understood. This study describes the global landscape of gene activities during wheat embryogenesis and grain development. Using comprehensive transcriptomic analyses of two wheat cultivars and three diploid grasses, we investigated gene expression at seven stages of embryo development, two endosperm stages, and one pericarp stage. We identified transcriptional signatures and developmental similarities and differences among the five species, revealing the evolutionary divergence of gene expression programs and the contributions of A, B, and D subgenomes to grain development in polyploid wheats. The characterization of embryonic transcriptional programming in hexaploid wheat, tetraploid wheat, and diploid grass species provides insight into the landscape of gene expression in modern wheat and its ancestral species. This study presents a framework for understanding the evolution of domesticated wheat and the selective pressures placed on grain production, with important implications for future performance and yield improvements.

Mutated genes are rarely common even in the same pathological type between cancer patients and as such, it has been very challenging to interpret genome sequencing data and difficult to predict clinical outcomes. PIK3CA is one of a few genes whose mutations are relatively popular in tumors. For example, more than 46.6% of luminal-A breast cancer samples have PIK3CA mutated, whereas only 35.5% of all breast cancer samples contain PIK3CA mutations. To understand the function of PIK3CA mutations in luminal A breast cancer, we applied our recently-proposed Cancer Hallmark Network Framework to investigate the network motifs in the PIK3CA-mutated luminal A tumors. We found that more than 70% of the PIK3CA-mutated luminal A tumors contain a positive regulatory loop where a master regulator (PDGF-D), a second regulator (FLT1) and an output node (SHC1) work together. Importantly, we found the luminal A breast cancer patients harboring the PIK3CA mutation and this positive regulatory loop in their tumors have significantly longer survival than those harboring PIK3CA mutation only in their tumors. These findings suggest that the underlying molecular mechanism of PIK3CA mutations in luminal A patients can participate in a positive regulatory loop, and furthermore the positive regulatory loop (PDGF-D/FLT1/SHC1) has a predictive power for the survival of the PIK3CA-mutated luminal A patients.

Over the past 2 decades, classification of tumors based on genomics has resulted in identifying distinct tumor molecular subtypes for cancer types, thereby providing a framework to study the molecular mechanisms of cancer. Tumor molecular subtypes hold the key to informing clinical outcomes and treatment. It has been well established that each cancer type has several distinct subtypes that are not shared with any other cancer type. Immune checkpoint therapy (ICT) has been able to successfully eliminate tumors in 10% to 40% of patients with many cancer types. However, in most patients, ICT has failed to have its intended effect.^1,2 It has been thought that an understanding of tumor-infiltrating lymphocytes (TILs) and the tumor immune microenvironment (TIME) could provide insights into ICT resistance and might thereby improve existing immunotherapies.^3,4 Therefore, with the advance of cancer immunotherapy, there is a strong interest in stratifying tumors into TIME subtypes. This stratification could provide better insights into the underlying molecular mechanisms for TILs and response to ICT as well as help to improve current immunotherapy. However, so far, there is no consensus about how to study TIMEs, and we lack efficient tools to stratify TIMEs.

Background: The incidence of breast cancer among young women (aged ≤40 years) has increased in North America and Europe. Fewer than 10% of cases among young women are attributable to inheritedBRCA1orBRCA2mutations, suggesting an important role for somatic mutations. This study investigated genomic differences between young- and older-onset breast tumours.Methods: In this study we characterized the mutational landscape of 89 young-onset breast tumours (≤40 years) and examined differences with 949 older-onset tumours (> 40 years) using data from The Cancer Genome Atlas. We examined mutated genes, mutational load, and types of mutations. We used complementary R packages “deconstructSigs” and “SomaticSignatures” to extract mutational signatures. A recursively partitioned mixture model was used to identify whether combinations of mutational signatures were related to age of onset.Results: Older patients had a higher proportion of mutations inPIK3CA,CDH1, andMAP3K1genes, while young-onset patients had a higher proportion of mutations inGATA3andCTNNB1. Mutational load was lower for young-onset tumours, and a higher proportion of these mutations were C > A mutations, but a lower proportion were C > T mutations compared to older-onset tumours. The most common mutational signatures identified in both age groups were signatures 1 and 3 from the COSMIC database. Signatures resembling COSMIC signatures 2 and 13 were observed among both age groups. We identified a class of tumours with a unique combination of signatures that may be associated with young age of onset.Conclusions: The results of this exploratory study provide some evidence that the mutational landscape and mutational signatures among young-onset breast cancer are different from those of older-onset patients. The characterization of young-onset tumours could provide clues to their etiology which may inform future prevention. Further studies are required to confirm our findings.

Department of Health Technology and Informatics2016-2017 > Academic research: refereed > Publication in refereed journalbcr

The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types) of the molecular networks, for example, genome-scale metabolic network (GMN), transcriptional regulatory network (TRN), and signal transduction network (STN). It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling) of a cell’s activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.