Cong Feng

Abstract Harnessing genetic diversity in major staple crops through the development of new breeding capabilities is essential to ensure food security 1 . Here we examined the genetic and phenotypic diversity of the A. E. Watkins landrace collection 2 of bread wheat ( Triticum aestivum ), a major global cereal, by whole-genome re-sequencing of 827 Watkins landraces and 208 modern cultivars and in-depth field evaluation spanning a decade. We found that modern cultivars are derived from two of the seven ancestral groups of wheat and maintain very long-range haplotype integrity. The remaining five groups represent untapped genetic sources, providing access to landrace-specific alleles and haplotypes for breeding. Linkage disequilibrium-based haplotypes and association genetics analyses link Watkins genomes to the thousands of identified high-resolution quantitative trait loci and significant marker–trait associations. Using these structured germplasm, genotyping and informatics resources, we revealed many Watkins-unique beneficial haplotypes that can confer superior traits in modern wheat. Furthermore, we assessed the phenotypic effects of 44,338 Watkins-unique haplotypes, introgressed from 143 prioritized quantitative trait loci in the context of modern cultivars, bridging the gap between landrace diversity and current breeding. This study establishes a framework for systematically utilizing genetic diversity in crop improvement to achieve sustainable food security.

Pneumonia is a lower respiratory tract infection that causes dramatic mortality worldwide. The present study aimed to investigate the pathogenesis of pneumonia and identify microRNA (miRNA) biomarkers as candidates for targeted therapy. RNA from the peripheral blood plasma of participants with pneumonia (severe, n=9; non‑severe, n=9) and controls (n=9) was isolated and paired‑end sequencing was performed on an Illumina HiSeq4000 system. Following the processing of raw reads, the sequences were aligned against the Genome Reference Consortium human genome assembly 38 reference genome using Bowtie2 software. Reads per kilobase of transcript per million mapped read values were obtained and the limma software package was used to identify differentially expressed miRNAs (DE‑miRs). Then, DE‑miR targets were predicted and subjected to enrichment analysis. In addition, a protein‑protein interaction (PPI) network of the predicted targets was constructed. This analysis identified 11 key DE‑miRs in pneumonia samples, including 6 upregulated miRNAs (including hsa‑miR‑34a and hsa‑miR‑455) and 5 downregulated miRNAs (including hsa‑let‑7f‑1). All DE‑miRs kept their upregulation/downregulation pattern in the control, non‑severe pneumonia and severe pneumonia samples. Predicted target genes of DE‑miRs in the subjects with non‑severe pneumonia vs. the control and the subjects with severe pneumonia vs. the non‑severe pneumonia group were markedly enriched in the adherens junction and Wnt signaling pathways. KALRN, Ras homolog family member A (RHOA), β‑catenin (CTNNB1), RNA polymerase II subunit K (POLR2K) and amyloid precursor protein (APP) were determined to encode crucial proteins in the PPI network constructed. KALRN was predicted to be a target of hsa‑mir‑200b, while RHOA, CTNNB1, POLR2K and APP were predicted targets of hsa‑let‑7f‑1. The results of the present study demonstrated that hsa‑let‑7f‑1 may serve a role in the development of cancer and the Notch signaling pathway. Conversely, hsa‑miR‑455 may be an inhibitor of pneumonia pathogenesis. Furthermore, hsa‑miR‑200b might promote pneumonia via targeting KALRN.

Many Internet users have encountered serious information overload problem on social networks such as Facebook and Twitter, where users can consume the streams of social updates from their social connections. Traditional methods solving this problem include collaborative filtering and information diffusion modeling. Both methods answer the "who will adopt what" question from different perspective, while either of them only captures single-faceted knowledge of evidences. In this paper, we solve the personalized social update recommendation problem by proposing a framework which integrates the advantages of collaborative filtering and the characteristics of diffusion processes. The main contributions of this paper are three folds. First, we propose a plenty of diffusion features which capture the characteristics of diffusion processes. Second, we build a joint model which takes the advantages of both collaborative filtering and the characteristics of diffusion processes for recommendation. Finally, experiments on two real-world datasets show that our joint model outperforms the methods capturing single-faceted knowledge and several other baselines.

BACKGROUND: Major depressive disorder (MDD) and interstitial cystitis (IC) are two highly debilitating conditions that often coexist with reciprocal effect, significantly exacerbating patients' suffering. However, the molecular underpinnings linking these disorders remain poorly understood. METHODS: Transcriptomic data from GEO datasets including those of MDD and IC patients was systematically analyzed to develop and validate our model. Following removal of batch effect, differentially expressed genes (DEGs) between respective disease and control groups were identified. Shared DEGs of the conditions then underwent functional enrichment analyses. Additionally, immune infiltration analysis was quantified through ssGSEA. A diagnostic model for MDD was constructed by exploring 113 combinations of 12 machine learning algorithms with 10-fold cross-validation on the training sets following by external validation on test sets. Finally, the "Enrichr" platform was utilized to identify potential drugs for MDD. RESULTS: Totally, 21 key genes closely associated with both MDD and IC were identified, predominantly involved in immune processes based on enrichment analyses. Immune infiltration analysis revealed distinct profiles of immune cell infiltration in MDD and IC compared to healthy controls. From these genes, a robust 11-gene (ABCD2, ATP8B4, TNNT1, AKR1C3, SLC26A8, S100A12, PTX3, FAM3B, ITGA2B, OLFM4, BCL7A) diagnostic signature was constructed, which exhibited superior performance over existing MDD diagnostic models both in training and testing cohorts. Additionally, epigallocatechin gallate and 10 other drugs emerged as potential targets for MDD. CONCLUSION: Our work developed a diagnostic model for MDD employing a combination of bioinformatic techniques and machine learning methods, focusing on shared genes between MDD and IC.

Wheat blast, caused by the fungus Magnaporthe oryzae, threatens global cereal production since its emergence in Brazil in 1985 and recently spread to Bangladesh and Zambia. Here we demonstrate that the AVR-Rmg8 effector, common in wheat-infecting isolates, is recognized by the gene Pm4, previously shown to confer resistance to specific races of Blumeria graminis f. sp. tritici, the cause of powdery mildew of wheat. We show that Pm4 alleles differ in their recognition of different AVR-Rmg8 alleles, and some confer resistance only in seedling leaves but not spikes, making it important to select for those alleles that function in both tissues. This study has identified a gene recognizing an important virulence factor present in wheat blast isolates in Bangladesh and Zambia and represents an important first step towards developing durably resistant wheat cultivars for these regions.