Identification of marker genes in Alzheimer's disease using a machine-learning model

Alzheimer''s Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.     

Integration of high-throughput analytics and cell imaging enables direct early productivity and product quality assessment during Chinese Hamster ovary cell line development for a complex multi-subunit vaccine antigen

Mammalian cell line generation typically includes stable pool generation, single cell cloning and several rounds of clone selection based on cell growth, productivity and product quality criteria. Individual clone expansion and phenotype-based ranking is performed initially for hundreds or thousands of mini-scale cultures, representing the major operational challenge during cell line development. Automated cell culture and analytics systems have been developed to enable high complexity clone selection workflows; while ensuring traceability, safety, and quality of cell lines intended for biopharmaceutical applications. Here we show that comprehensive and quantitative assessment of cell growth, productivity, and product quality attributes are feasible at the 200–1,200 cell colony stage, within 14 days of the single cell cloning in static 96-well plate culture. The early cell line characterization performed prior to the clone expansion in suspension culture can be used for a single-step, direct selection of high quality clones. Such clones were comparable, both in terms of productivity and critical quality attributes (CQAs), to the top-ranked clones identified using an established iterative clone screening approach. Using a complex, multi-subunit antigen as a model protein, we observed stable CQA profiles independently of the cell culture format during the clonal expansion as well as in the batch and fed-batch processes. In conclusion, we propose an accelerated clone selection approach that can be readily incorporated into various cell line development workstreams, leading to significant reduction of the project timelines and resource requirements.     

Acute treatment with relaxin protects the kidney against ischaemia/reperfusion injury

Although recent preclinical and clinical studies have demonstrated that recombinant human relaxin (rhRLX) may have important therapeutic potential in acute heart failure and chronic kidney diseases, the effects of acute rhRLX administration against renal ischaemia/reperfusion (I/R) injury have never been investigated. Using a rat model of 1‐hr bilateral renal artery occlusion followed by 6‐hr reperfusion, we investigated the effects of rhRLX (5 μg/Kg i.v.) given both at the beginning and after 3 hrs of reperfusion. Acute rhRLX administration attenuated the functional renal injury (increase in serum urea and creatinine), glomerular dysfunction (decrease in creatinine clearance) and tubular dysfunction (increase in urinary excretion of N‐acetyl‐β‐glucosaminidase) evoked by renal I/R. These beneficial effects were accompanied by a significant reduction in local lipid peroxidation, free radical‐induced DNA damage and increase in the expression/activity of the endogenous antioxidant enzymes Mn‐ and CuZn‐superoxide dismutases (SOD). Furthermore, rhRLX administration attenuated the increase in leucocyte activation, as suggested by inhibition of myeloperoxidase activity, intercellular‐adhesion‐molecule‐1 expression, interleukin (IL)‐1β, IL‐18 and tumour necrosis factor‐α production as well as increase in IL‐10 production. Interestingly, the reduced oxidative stress status and neutrophil activation here reported were associated with rhRLX‐induced activation of endothelial nitric oxide synthase and up‐regulation of inducible nitric oxide synthase, possibly secondary to activation of Akt and the extracellular signal‐regulated protein kinase (ERK) 1/2, respectively. Thus, we report herein that rhRLX protects the kidney against I/R injury by a mechanism that involves changes in nitric oxide signalling pathway.     

Incorporation of SPION-casein core-shells into silk-fibroin nanofibers for cardiac tissue engineering

Mimicking the structure of extracellular matrix (ECM) of myocardium is necessary for fabrication of functional cardiac tissue. The superparamagnetic iron oxide nanoparticles (SPIONs, Fe₃O₄), as new generation of magnetic nanoparticles (NPs), are highly intended in biomedical studies. Here, SPION NPs (1 wt%) were synthesized and incorporated into silk-fibroin (SF) electrospun nanofibers to enhance mechanical properties and topography of the scaffolds. Then, the mouse embryonic cardiac cells (ECCs) were seeded on the scaffolds for in vitro studies. The SPION NPs were studied by scanning electron microscope (SEM), X-ray diffraction (XRD), and transmission electron microscope (TEM). SF nanofibers were characterized after incorporation of SPIONs by SEM, TEM, water contact angle measurement, and tensile test. Furthermore, cytocompatibility of scaffolds was confirmed by 3-(4, 5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. SEM images showed that ECCs attached to the scaffolds with elongated morphologies. Also, the real-time PCR and immunostaining studies approved upregulation of cardiac functional genes in ECCs seeded on the SF/SPION-casein scaffolds including GATA-4, cardiac troponin T, Nkx 2.5, and alpha-myosin heavy chain, compared with the ones in SF. In conclusion, incorporation of core-shells in SF supports cardiac differentiation, while has no negative impact on ECCs' proliferation and self-renewal capacity.     

Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated “The Cancer Proteome Atlas” (TCPA), which contains reverse-phase protein arrays–based high-quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.