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Background Idiopathic pulmonary fibrosis (IPF) is a chronic respiratory disease with high incidence rate, morbidity and mortality. Jinshui Huanxian formula (JHF) is an empirical formula for the pathogenesis of lung-kidney qi deficiency and phlegm-blood stasis in pulmonary fibrosis. The purpose of this study is to explore the pharmacological mechanism of JHF action in IPF therapy by network interaction analysis. Methods The main active components and corresponding target genes of JHF were predicted using various databases. Two sets of IPF disease genes were obtained from the DisGeNET database and GEO database. Two sets of drug targets for IPF treatment were collected and the overlapping genes between disease genes and drug targets were analyzed. The target genes of JHF were intersected with the differentially expressed genes of IPF to obtain the predicted targets of JHF acting on IPF. The functions and pathways of predicted targets acting on IPF were analyzed by using DAVID and KEGG pathway database. Finally, the resulting drug target mechanisms were validated in a rat model of pulmonary fibrosis. Results 494 active compounds and 1304 corresponding targets were screened. Intersection analysis showed that 4 genes were common genes of JHF targets, IPF disease genes and anti-IPF drugs in KEGG database, and these genes were targeted by several compounds of JHF respectively. 72 JHF targets were closely related with IPF, and were thus considered therapeutically relevant. The targets were screened and participated in the regulation of IPF through 18 pathways. The molecular functions of targets included regulation of oxidoreductase activity, kinase regulator activity, phosphotransferase activity and transmembrane receptor protein kinase activity. In vivo experiments showed that JHF could alleviate the degree of pulmonary fibrosis, including the decrease of collagen deposition and epithelial-mesenchymal transition. Conclusions This study explored the mechanisms of JHF from a systematic point of view, trying to identify the specific target pathways acing on IPF. Pharmacological network with in vivo validation explained the potential roles and mechanisms of JHF in IPF therapy.

Replicating circular RNAs are independent plant pathogens known as viroids, or act to modulate the pathogenesis of plant and animal viruses as their satellite RNAs. The rate of discovery of these subviral pathogens was low over the past 40 years because the classical approaches are technical demanding and time-consuming. We previously described an approach for homology-independent discovery of replicating circular RNAs by analysing the total small RNA populations from samples of diseased tissues with a computational program known as progressive filtering of overlapping small RNAs (PFOR). However, PFOR written in PERL language is extremely slow and is unable to discover those subviral pathogens that do not trigger in vivo accumulation of extensively overlapping small RNAs. Moreover, PFOR is yet to identify a new viroid capable of initiating independent infection. Here we report the development of PFOR2 that adopted parallel programming in the C++ language and was 3 to 8 times faster than PFOR. A new computational program was further developed and incorporated into PFOR2 to allow the identification of circular RNAs by deep sequencing of long RNAs instead of small RNAs. PFOR2 analysis of the small RNA libraries from grapevine and apple plants led to the discovery of Grapevine latent viroid (GLVd) and Apple hammerhead viroid-like RNA (AHVd-like RNA), respectively. GLVd was proposed as a new species in the genus Apscaviroid, because it contained the typical structural elements found in this group of viroids and initiated independent infection in grapevine seedlings. AHVd-like RNA encoded a biologically active hammerhead ribozyme in both polarities, and was not specifically associated with any of the viruses found in apple plants. We propose that these computational algorithms have the potential to discover novel circular RNAs in plants, invertebrates and vertebrates regardless of whether they replicate and/or induce the in vivo accumulation of small RNAs., Author Summary Viroids are a unique class of subviral pathogens found in plants, and they are difficult to identify since they are free circular non-coding RNAs and often replicate to low levels in host cells. We previously described the computational algorithm PFOR that discovers viroids by analyzing total small RNAs of the infected plants obtained by next-generation sequencing platforms. However, the algorithm written in PERL language is very slow, and viroid identification depends on the in vivo accumulation of extensively overlapping sets of small RNAs to target viroids. Here we report the development of PFOR2 that adopted parallel programming in the C++ language and was significantly faster than PFOR. We also describe a simple computational program that after incorporation into PFOR2 is capable of identifying viroids from deep sequencing of long RNAs instead of small RNAs. Moreover, we report the identification of Grapevine latent viroid (GLVd) and Apple hammerhead viroid-like RNA by the computational approach. Since our new algorithms do not depend on the analysis of viroid-derived small RNAs produced in vivo, it is possible to discover viroids in a wide range of host species including plants, invertebrates and vertebrates.

Background The invasion of plant by viruses cause major damage to plants and reduces crop yield and integrity. Devastating plant virus infection has been experienced at different times all over the world, which are attributed to different events of mutation, re-assortment and recombination occurring in the viruses. Strategies for proper virus management has been mostly limited to eradicating the vectors that spreads the plant viruses. However, development of prompt and effective diagnostic methods are required to monitor emerging and re-emerging diseases that may be symptomatic or asymptomatic in the plant as well as the genetic variation and evolution in the plant viruses. A survey of plant viruses infecting field-grown Tobacco crop was conducted in Anhui Province of China by the deep sequencing of sRNAs. Methods Survey of plant viruses infecting Tobacco was carried based on 104 samples collected across the province. Nine different sRNA libraries was prepared and custom-made bioinformatics pipeline coupled with molecular techniques was developed to sequence, assemble and analyze the siRNAs for plant virus discovery. We also carried out phylogenetic and recombination analysis of the identified viruses. Results Twenty two isolates from eight different virus species including Cucumber mosaic virus, Potato virus Y, Tobacco mosaic virus, Tobacco vein banding Mosaic virus, Pepper mottle virus, Brassica yellow virus, Chilli venial mottle virus, Broad bean wilt virus 2 were identified in tobacco across the survey area. The near-complete genome sequence of the 22 new isolates were determined and analyzed. The isolates were grouped together with known strains in the phylogenetic tree. Molecular variation in the isolates indicated the conserved coding regions have majorly a nucleotide sequence identity of 80-94 % with previously identified isolates. Various events of recombination were discovered among some of the isolates indicating that two or more viruses or different isolates of one virus infect the same host cell. Conclusion This study describes the discovery of a consortium of plant viruses infecting Tobacco that are broadly distributed in Anhui province of China. It also demonstrates the effectiveness of NGS in identifying plant viruses without a prior knowledge of the virus and the genetic diversity that enhanced mixed infection. Electronic supplementary material The online version of this article (doi:10.1186/s12985-016-0639-7) contains supplementary material, which is available to authorized users.