Objectives

Numerous software has been developed to infer the gene regulatory network, a long-standing key topic in biology and computational biology. Yet the slowness and inaccuracy inherited in current software hamper their applications to the increasing massive data. Here, we develop a software, FINET (Fast Inferring NETwork), to infer a network with high accuracy and rapidity from big data.

Results

The high accuracy results from integrating algorithms with stability-selection, elastic-net, and parameter optimization. Tested by a known biological network, FINET infers interactions with over 94% precision. The high speed comes from partnering parallel computations implemented with Julia, a new compiled language that runs much faster than existing languages used in the current software, such as R, Python, and MATLAB. Regardless of FINET's implementations with Julia, users with no background in the language or computer science can easily operate it, with only a user-friendly single command line. In addition, FINET can infer other networks such as chemical networks and social networks. Overall, FINET provides a confident way to efficiently and accurately infer any type of network for any scale of data.

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems.

Zika virus (ZIKV) is an emerging RNA virus in the widespread Flavivirus genus. Recently, ZIKV has rapidly spread around the world and has been implicated in human disease, including neurological disorders, triggering public and scientific attention. Understanding how ZIKV causes disease is the highest priority, yet little is known about this virus. Here we examine the currently published data from ZIKV studies to provide the latest understanding of ZIKV genome biology and molecular pathogenesis. The ZIKV genome evolved rapidly from the Flavivirus genus and diverged from the members of this genus, even within the dengue virus cluster to which ZIKV belongs. Genome variations and divergences also exist among ZIKV strains/isolates. These genome divergences might account for the uniqueness of Zika disease. ZIKV infection activates not only the antiviral immune response but also the pro-inflammatory responses associated with disease symptoms. Strikingly, ZIKV activates protein complexes that are functionally associated with disease process, such as glial cell activation and proliferation (for example, Toll-like receptors), apoptosis and cell death, and inflammation. The activation of these complexes may critically contribute to Zika disease. The novel insights into ZIKV genome divergence and disease mechanisms summarized in this review will help accelerate the development of anti-ZIKV strategies.

Chlamydia pneumoniae is responsible for a high prevalence of respiratory infections worldwide and has been implicated in atherosclerosis. Inflammation is regulated by transcription factor (TF) networks. Yet, the core TF network triggered by chlamydiae remains largely unknown. Primary human coronary artery endothelial cells were mock-infected or infected with C. pneumoniae to generate human transcriptome data throughout the chlamydial developmental cycle. Using systems network analysis, the predominant TF network involved receptor, binding and adhesion and immune response complexes. Cells transfected with interfering RNA against activator protein-1 (AP-1) members FOS, FOSB, JUN and JUNB had significantly decreased expression and protein levels of inflammatory mediators interleukin (IL)6, IL8, CD38 and tumour necrosis factor compared with controls. These mediators have been shown to be associated with C. pneumoniae disease. Expression of AP-1 components was regulated by MAPK3K8, a MAPK pathway component. Additionally, knock-down of JUN and FOS showed significantly decreased expression of Toll-like receptor (TLR)3 during infection, implicating JUN and FOS in TLR3 regulation. TLR3 stimulation led to elevated IL8. These findings suggest that C. pneumoniae initiates signalling via TLR3 and MAPK that activate AP-1, a known immune activator in other bacteria not previously shown for chlamydiae, triggering inflammation linked to C. pneumoniae disease.

Chlamydia pneumoniae is responsible for a high prevalence of respiratory infections worldwide and has been implicated in atherosclerosis. Inflammation is regulated by transcription factor (TF) networks. Yet, the core TF network triggered by chlamydiae remains largely unknown. Primary human coronary artery endothelial cells were mock-infected or infected with C. pneumoniae to generate human transcriptome data throughout the chlamydial developmental cycle. Using systems network analysis, the predominant TF network involved receptor, binding and adhesion and immune response complexes. Cells transfected with interfering RNA against activator protein-1 (AP-1) members FOS, FOSB, JUN and JUNB had significantly decreased expression and protein levels of inflammatory mediators interleukin (IL)6, IL8, CD38 and tumour necrosis factor compared with controls. These mediators have been shown to be associated with C. pneumoniae disease. Expression of AP-1 components was regulated by MAPK3K8, a MAPK pathway component. Additionally, knock-down of JUN and FOS showed significantly decreased expression of Toll-like receptor (TLR)3 during infection, implicating JUN and FOS in TLR3 regulation. TLR3 stimulation led to elevated IL8. These findings suggest that C. pneumoniae initiates signalling via TLR3 and MAPK that activate AP-1, a known immune activator in other bacteria not previously shown for chlamydiae, triggering inflammation linked to C. pneumoniae disease.

Whilst many chronic graft versus host disease (cGVHD) biomarkers have been previously reported, few have been verified in an independent cGVHD cohort. We aimed to verify the diagnostic accuracy of previously reported markers of cGVHD in a multi-centre Chronic GVHD Consortium. A total of 42 RNA and 18 protein candidate biomarkers were assessed amongst 59 cGVHD cases and 33 matched non-GVHD controls. Total RNA was isolated from PBMC, and RNA markers were quantified using PCR. Serum protein markers were quantified using ELISA. A combined 3 RNA biomarker (IRS2, PLEKHF1 and IL1R2) and 2 clinical variables (recipient CMV serostatus and conditioning regimen intensity) panel accurately (AUC 0.81) segregated cGVHD cases from controls. Other studied RNA and protein markers were not confirmed as accurate cGVHD diagnostic biomarkers. The studied markers failed to segregate higher risk cGVHD (per overall NIH 0-3 score, and overlap versus classic cGVHD status). These data support the need for multiple independent verification studies for the ultimate clinical application of cGVHD diagnostic biomarkers.

The human urinary proteome provides an assessment of kidney injury with specific biomarkers for different kidney injury phenotypes. In an effort to fully map and decipher changes in the urine proteome and peptidome after kidney transplantation, renal allograft biopsy matched urine samples were collected from 396 kidney transplant recipients. Centralized and blinded histology data from paired graft biopsies was used to classify urine samples into diagnostic categories of acute rejection, chronic allograft nephropathy, BK virus nephritis, and stable graft. A total of 245 urine samples were analyzed by liquid chromatography-mass spectrometry using isobaric Tags for Relative and Absolute Quantitation (iTRAQ) reagents. From a group of over 900 proteins identified in transplant injury, a set of 131 peptides were assessed by selected reaction monitoring for their significance in accurately segregating organ injury causation and pathology in an independent cohort of 151 urine samples. Ultimately, a minimal set of 35 proteins were identified for their ability to segregate the 3 major transplant injury clinical groups, comprising the final panel of 11 urinary peptides for acute rejection (93% area under the curve [AUC]), 12 urinary peptides for chronic allograft nephropathy (99% AUC), and 12 urinary peptides for BK virus nephritis (83% AUC). Thus, urinary proteome discovery and targeted validation can identify urine protein panels for rapid and noninvasive differentiation of different causes of kidney transplant injury, without the requirement of an invasive biopsy.