No abstract

IL-35 is a newly discovered inhibitory cytokine secreted by regulatory T cells (Tregs) and may have therapeutic potential in several inflammatory disorders. Acute graft-versus-host disease (aGVHD) is a major complication of allogeneic hematopoietic stem cell transplantation and caused by donor T cells and inflammatory cytokines. The role of IL-35 in aGVHD is still unknown. Here we demonstrate that IL-35 overexpression suppresses CD4+ effector T cell activation, leading to a reduction in alloreactive T-cell responses and aGVHD severity. It also leads to the expansion of CD4+Foxp3+ Tregs in the aGVHD target organs. Furthermore, IL-35 overexpression results in a selective decrease in the frequency of Th1 cells and an increase of IL-10-producing CD4+ T cells in aGVHD target tissues. Serum levels of TNF-α, IFN-γ, IL-6, IL-22 and IL-23 decrease and IL-10 increases in response to IL-35. Most importantly, IL-35 preserves graft versus leukemia effect. Finally, aGVHD grade 2-4 patients have decreased serum IL-35 levels comparing with time-matched patients with aGVHD grade 0-1. Our findings indicate that IL-35 plays an important role in reducing aGVHD through promoting the expansion of Tregs and repressing Th1 responses, and should be investigated as the therapeutic strategy for aGVHD.

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research.

Functional relationship networks, which reveal the collaborative roles between genes, have significantly accelerated our understanding of gene functions and phenotypic relevance. However, establishing such networks for alternatively spliced isoforms remains a difficult, unaddressed problem due to the lack of systematic functional annotations at the isoform level, which renders most supervised learning methods difficult to be applied to isoforms. Here we describe a novel multiple instance learning-based probabilistic approach that integrates large-scale, heterogeneous genomic datasets, including RNA-seq, exon array, protein docking and pseudo-amino acid composition, for modeling a global functional relationship network at the isoform level in the mouse. Using this approach, we formulate a gene pair as a set of isoform pairs of potentially different properties. Through simulation and cross-validation studies, we showed the superior accuracy of our algorithm in revealing the isoform-level functional relationships. The local networks reveal functional diversity of the isoforms of the same gene, as demonstrated by both large-scale analyses and experimental and literature evidence for the disparate functions revealed for the isoforms of Ptbp1 and Anxa6 by our network. Our work can assist the understanding of the diversity of functions achieved by alternative splicing of a limited set of genes in mammalian genomes, and may shift the current gene-centered network prediction paradigm to the isoform level. Author summaryProteins carry out their functions through interacting with each other. Such interactions can be achieved through direct physical interactions, genetic interactions, or co-regulation. To summarize these interactions, researches have established functional relationship networks, in which each gene is represented as a node and the connections between the nodes represent how likely two genes work in the same biological process. Currently, these networks are established at the gene level only, while each gene, in mammalian systems, can be alternatively spliced into multiple isoforms that may have drastically different interaction partners. This information can be mined through integrating data that provide isoform-level information, such as RNA-seq and protein docking scores predicted from amino acid sequences. In this study, we developed a novel algorithm to integrate such data for predicting isoform-level functional relationship networks, which allows us to investigate the collaborative roles between genes at a high resolution.