Comparative Effectiveness of Combined IgM-Enriched Immunoglobulin and Extracorporeal Blood Purification Plus Standard Care Versus Standard Care for Sepsis and Septic Shock after Cardiac Surgery.

Background: The combination of surgery, bacterial spread-out, and artificial cardiopulmonary bypass surfaces results in a release of key inflammatory mediators leading to an overshooting systemic hyper-inflammatory condition frequently associated with compromised hemodynamics and organ dysfunction. A promising approach could be extracorporeal blood purification therapies in combination with IgM enriched immunoglobulin. This approach might perform a balanced control of both hyper and hypo-inflammatory phases as an immune-modulating intervention. Methods: We performed a retrospective observational study of patients with proven infection after cardiac surgery between January 2020 and December 2021. Patients were divided into two groups: (1) the first group (Control Group) followed a standard care approach as recommended by the Surviving Sepsis Campaign Guidelines; The second group (Active Group) underwent extracorporeal blood purification therapy (EBPT) in combination with intravenous administration of IgM enriched immunoglobulin 5 mL/kg die for at least three consecutive days, in conjunction with the standard approach (SSC Guidelines). In addition, ventriculo-arterial (V/A) coupling, Interleukin 6 (IL-6), Endotoxin Activity Assay (EAA), Procalcitonin, White Blood Cells (WBC) counts, Sequential Organ Failure Assessment (SOFA) Score and Inotropic Score were assessed in both two groups at different time points. Results: Fifty-four patients were recruited; 25 were in the Control Group, while 29 participants were in the Active Group. SOFA score significantly improved from baseline [12 (9-16)] until at T 3 [8 (3-13)] in the active group; it was associated with a median EAA reduction from 1.03 (0.39-1.20) at T 0 to 0.41 (0.2-0.9) at T 3 in the active group compared with control group 0.70 (0.50-1.00) at T 0 to 0.70 (0.50-1.00) at T 3 (p < 0.001). V/A coupling tended to be lower in patients of the active arm ranging from 1.9 (1.2-2.7) at T 0 to 0.8 (0.8-2.2) at T 3 than in those of the control arm ranging from 2.1 (1.4-2.2) at T0 to 1.75 (1.45-2.1) at T 3 (p = 0.099). The hemodynamic improvement over time was associated with evident but no significant decrease in inotropic score in the active group compared with the control group. Changes in EAA value from T 0 to T 4 were directly and significantly related (r = 0.39, p = 0.006) to those of V/A coupling. Conclusions: EBPT, in combination with IgM enriched immunoglobulin, was associated with a mitigated postoperative response of key cytokines with a significant decrease in IL-6, Procalcitonin, and EAA and was associated with improvement of clinical and metabolic parameters.

A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks.

BACKGROUND: Reverse engineering of transcriptional regulatory networks (TRN) from genomics data has always represented a computational challenge in System Biology. The major issue is modeling the complex crosstalk among transcription factors (TFs) and their target genes, with a method able to handle both the high number of interacting variables and the noise in the available heterogeneous experimental sources of information. RESULTS: In this work, we propose a data fusion approach that exploits the integration of complementary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-scale transcriptional networks. We develop a hybrid structure-learning algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia to reconstruct TRNs in a genome-wide perspective. Applying our method to high-throughput data, we verified its ability to deal with the complexity of a genomic TRN, providing a snapshot of the synergistic TFs regulatory activity. Given the noisy nature of data-driven prior knowledge, which potentially contains incorrect information, we also tested the method's robustness to false priors on a benchmark dataset, comparing the proposed approach to other regulatory network reconstruction algorithms. We demonstrated the effectiveness of our framework by evaluating structural commonalities of our learned genomic network with other existing networks inferred by different DNA binding information-based methods. CONCLUSIONS: This Bayesian omics-data fusion based methodology allows to gain a genome-wide picture of the transcriptional interplay, helping to unravel key hierarchical transcriptional interactions, which could be subsequently investigated, and it represents a promising learning approach suitable for multi-layered genomic data integration, given its robustness to noisy sources and its tailored framework for handling high dimensional data.

Protease target prediction via matrix factorization.

MOTIVATION: Protein cleavage is an important cellular event, involved in a myriad of processes, from apoptosis to immune response. Bioinformatics provides in silico tools, such as machine learning-based models, to guide the discovery of targets for the proteases responsible for protein cleavage. State-of-the-art models have a scope limited to specific protease families (such as Caspases), and do not explicitly include biological or medical knowledge (such as the hierarchical protein domain similarity or gene-gene interactions). To fill this gap, we present a novel approach for protease target prediction based on data integration. RESULTS: By representing protease-protein target information in the form of relational matrices, we design a model (i) that is general and not limited to a single protease family, and (b) leverages on the available knowledge, managing extremely sparse data from heterogeneous data sources, including primary sequence, pathways, domains and interactions. When compared with other algorithms on test data, our approach provides a better performance even for models specifically focusing on a single protease family. AVAILABILITY AND IMPLEMENTATION: https://gitlab.com/smarini/MaDDA/ (Matlab code and utilized data.). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Correction: A Network-Based Data Integration Approach to Support Drug Repurposing and Multi-Target Therapies in Triple Negative Breast Cancer.

[This corrects the article DOI: 10.1371/journal.pone.0162407.].

A Network-Based Data Integration Approach to Support Drug Repurposing and Multi-Target Therapies in Triple Negative Breast Cancer.

The integration of data and knowledge from heterogeneous sources can be a key success factor in drug design, drug repurposing and multi-target therapies. In this context, biological networks provide a useful instrument to highlight the relationships and to model the phenomena underlying therapeutic action in cancer. In our work, we applied network-based modeling within a novel bioinformatics pipeline to identify promising multi-target drugs. Given a certain tumor type/subtype, we derive a disease-specific Protein-Protein Interaction (PPI) network by combining different data-bases and knowledge repositories. Next, the application of suitable graph-based algorithms allows selecting a set of potentially interesting combinations of drug targets. A list of drug candidates is then extracted by applying a recent data fusion approach based on matrix tri-factorization. Available knowledge about selected drugs mechanisms of action is finally exploited to identify the most promising candidates for planning in vitro studies. We applied this approach to the case of Triple Negative Breast Cancer (TNBC), a subtype of breast cancer whose biology is poorly understood and that lacks of specific molecular targets. Our "in-silico" findings have been confirmed by a number of in vitro experiments, whose results demonstrated the ability of the method to select candidates for drug repurposing.