The common carp, Cyprinus carpio, is one of the most important cyprinid species and globally accounts for 10% of freshwater aquaculture production. Here we present a draft genome of domesticated C. carpio (strain Songpu), whose current assembly contains 52,610 protein-coding genes and approximately 92.3% coverage of its paleotetraploidized genome (2n = 100). The latest round of whole-genome duplication has been estimated to have occurred approximately 8.2 million years ago. Genome resequencing of 33 representative individuals from worldwide populations demonstrates a single origin for C. carpio in 2 subspecies (C. carpio Haematopterus and C. carpio carpio). Integrative genomic and transcriptomic analyses were used to identify loci potentially associated with traits including scaling patterns and skin color. In combination with the high-resolution genetic map, the draft genome paves the way for better molecular studies and improved genome-assisted breeding of C. carpio and other closely related species.

Cancer is a collection of genetic diseases, with large phenotypic differences and genetic heterogeneity between different types of cancers and even within the same cancer type. Recent advances in genome-wide profiling provide an opportunity to investigate global molecular changes during the development and progression of cancer. Meanwhile, numerous statistical and machine learning algorithms have been designed for the processing and interpretation of high-throughput molecular data. Molecular subtyping studies have allowed the allocation of cancer into homogeneous groups that are considered to harbor similar molecular and clinical characteristics. Furthermore, this has helped researchers to identify both actionable targets for drug design as well as biomarkers for response prediction. In this review, we introduce five frequently applied techniques for generating molecular data, which are microarray, RNA sequencing, quantitative polymerase chain reaction, NanoString and tissue microarray. Commonly used molecular data for cancer subtyping and clinical applications are discussed. Next, we summarize a workflow for molecular subtyping of cancer, including data preprocessing, cluster analysis, supervised classification and subtype characterizations. Finally, we identify and describe four major challenges in the molecular subtyping of cancer that may preclude clinical implementation. We suggest that standardized methods should be established to help identify intrinsic subgroup signatures and build robust classifiers that pave the way toward stratified treatment of cancer patients.

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer related death in the world with a five-year survival rate of less than 5%. Not all PDAC are the same, because there exist intra-tumoral heterogeneity between PDAC, which poses a great challenge to personalized treatments for PDAC. To dissect the molecular heterogeneity of PDAC, we performed a retrospective meta-analysis on whole transcriptome data from more than 1200 PDAC patients. Subtypes were identified based on non-negative matrix factorization (NMF) biclustering method. We used the gene set enrichment analysis (GSEA) and survival analysis to conduct the molecular and clinical characterization of the identified subtypes, respectively. Six molecular and clinical distinct subtypes of PDAC: L1-L6, are identified and grouped into tumor-specific (L1, L2 and L6) and stroma-specific subtypes (L3, L4 and L5). For tumor-specific subtypes, L1 (~ 22%) has enriched carbohydrate metabolism-related gene sets and has intermediate survival. L2 (~ 22%) has the worst clinical outcomes, and is enriched for cell proliferation-related gene sets. About 23% patients can be classified into L6, which leads to intermediate survival and is enriched for lipid and protein metabolism-related gene sets. Stroma-specific subtypes may contain high non-epithelial contents such as collagen, immune and islet cells, respectively. For instance, L3 (~ 12%) has poor survival and is enriched for collagen-associated gene sets. L4 (~ 14%) is enriched for various immune-related gene sets and has relatively good survival. And L5 (~ 7%) has good clinical outcomes and is enriched for neurotransmitter and insulin secretion related gene sets. In the meantime, we identified 160 subtype-specific markers and built a deep learning-based classifier for PDAC. We also applied our classification system on validation datasets and observed much similar molecular and clinical characteristics between subtypes. Our study is the largest cohort of PDAC gene expression profiles investigated so far, which greatly increased the statistical power and provided more robust results. We identified six molecular and clinical distinct subtypes to describe a more complete picture of the PDAC heterogeneity. The 160 subtype-specific markers and a deep learning based classification system may be used to better stratify PDAC patients for personalized treatments.

Microorganisms colonize the human body. The lungs and respiratory tract, previously believed to be sterile, harbor diverse microbial communities and the genomes of bacteria (bacteriome), viruses (virome), and fungi (mycobiome). Recent advances in amplicon and shotgun metagenomic sequencing technologies and data-analyzing methods have greatly aided the identification and characterization of microbial populations from airways. The respiratory microbiome has been shown to play roles in human health and disease and is an area of rapidly emerging interest in pulmonary medicine. In this review, we provide updated information in the field by focusing on four lung conditions, including asthma, chronic obstructive pulmonary disease, cystic fibrosis, and idiopathic pulmonary fibrosis. We evaluate gut, oral, and upper airway microbiomes and how they contribute to lower airway flora. The discussion is followed by a systematic review of the lower airway microbiome in health and disease. We conclude with promising research avenues and implications for evolving therapeutics.

Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis of all common cancers. However, divergent outcomes exist between patients, suggesting distinct underlying tumor biology. Here, we delineated this heterogeneity, compared interconnectivity between classification systems, and experimentally addressed the tumor biology that drives poor outcome. RNA-sequencing of 90 resected specimens and unsupervised classification revealed four subgroups associated with distinct outcomes. The worst-prognosis subtype was characterized by mesenchymal gene signatures. Comparative (network) analysis showed high interconnectivity with previously identified classification schemes and high robustness of the mesenchymal subtype. From species-specific transcript analysis of matching patient-derived xenografts we constructed dedicated classifiers for experimental models. Detailed assessments of tumor growth in subtyped experimental models revealed that a highly invasive growth pattern of mesenchymal subtype tumor cells is responsible for its poor outcome. Concluding, by developing a classification system tailored to experimental models, we have uncovered subtype-specific biology that should be further explored to improve treatment of a group of PDAC patients that currently has little therapeutic benefit from surgical treatment.

Amur ide (Leuciscus waleckii) is an economically and ecologically important species in Northern Asia. The Dali Nor population inhabiting Dali Nor Lake, a typical saline-alkaline lake in Inner Mongolia, is well-known for its adaptation to extremely high alkalinity. Genome information is needed for conservation and aquaculture purposes, as well as to gain further understanding into the genetics of stress tolerance. The objective of the study is to sequence the transcriptome and obtain a well-assembled transcriptome of Amur ide. The transcriptome of Amur ide was sequenced using the Illumina platform and assembled into 53,632 cDNA contigs, with an average length of 647 bp and a N50 length of 1,094 bp. A total of 19,338 unique proteins were identified, and gene ontology and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses classified all contigs into functional categories. Open Reading Frames (ORFs) were detected from 34,888 (65.1%) of contigs with an average length of 577 bp, while 9,638 full-length cDNAs were identified. Comparative analyses revealed that 31,790 (59.3%) contigs have a significant similarity to zebrafish proteins, and 27,096 (50.5%), 27,524 (51.3%) and 27,996 (52.2%) to teraodon, medaka and three-spined stickleback proteins, respectively. A total of 10,395 microsatellites and 34,299 SNPs were identified and classified. A dN/dS analysis on unigenes was performed, which identified that 61 of the genes were under strong positive selection. Most of the genes are associated with stress adaptation and immunity, suggesting that the extreme alkaline-saline environment resulted in fast evolution of certain genes. The transcriptome of Amur ide had been deeply sequenced, assembled and characterized, providing a valuable resource for a better understanding of the Amur ide genome. The transcriptome data will facilitate future functional studies on the Amur ide genome, as well as provide insight into potential mechanisms for adaptation to an extreme alkaline-saline environment.