We investigated whether permeability transition-mediated release of mitochondrial cytochrome c is a potential therapeutic target for treating acute spinal cord injury (SCI). Based on previous reports, minocycline, a second-generation tetracycline, exerts neuroprotection partially by inhibiting mitochondrial cytochrome c release and reactive microgliosis. We first evaluated cytochrome c release at the injury epicenter after a T10 contusive SCI in rats. Cytochrome c release peaked at Ϸ4 -8 h postinjury. A dose-response study generated a safe pharmacological regimen that enabled i.p. minocycline to significantly lower cytosolic cytochrome c at the epicenter 4 h after SCI. In the long-term study, i.p. minocycline (90 mg͞kg administered 1 h after SCI followed by 45 mg͞kg administered every 12 h for 5 days) markedly enhanced long-term hind limb locomotion relative to that of controls. Coordinated motor function and hind limb reflex recoveries also were improved significantly. Histopathology suggested that minocycline treatment alleviated later-phase tissue loss, with significant sparing of white matter and ventral horn motoneurons at levels adjacent to the epicenter. Furthermore, glial fibrillary acidic protein and 2 ,3 cyclic nucleotide 3 phosphodiesterase immunocytochemistry showed an evident reduction in astrogliosis and enhanced survival of oligodendrocytes. Therefore, release of mitochondrial cytochrome c is an important secondary injury mechanism in SCI. Drugs with multifaceted effects in antagonizing this process and microgliosis may protect a proportion of spinal cord tissue that is clinically significant for functional recovery. Minocycline, with its proven clinical safety, capability to cross the blood-brain barrier, and demonstrated efficacy during a clinically relevant therapeutic window, may become an effective therapy for acute SCI.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.

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BackgroundTo understand cardiac and skeletal muscle function, it is important to define and explore their molecular constituents and also to identify similarities and differences in the gene expression in these two different striated muscle tissues. Here, we have investigated the genes and proteins with elevated expression in cardiac and skeletal muscle in relation to all other major human tissues and organs using a global transcriptomics analysis complemented with antibody-based profiling to localize the corresponding proteins on a single cell level.ResultsOur study identified a comprehensive list of genes expressed in cardiac and skeletal muscle. The genes with elevated expression were further stratified according to their global expression pattern across the human body as well as their precise localization in the muscle tissues. The functions of the proteins encoded by the elevated genes are well in line with the physiological functions of cardiac and skeletal muscle, such as contraction, ion transport, regulation of membrane potential and actomyosin structure organization. A large fraction of the transcripts in both cardiac and skeletal muscle correspond to mitochondrial proteins involved in energy metabolism, which demonstrates the extreme specialization of these muscle tissues to provide energy for contraction.ConclusionsOur results provide a comprehensive list of genes and proteins elevated in striated muscles. A number of proteins not previously characterized in cardiac and skeletal muscle were identified and localized to specific cellular subcompartments. These proteins represent an interesting starting point for further functional analysis of their role in muscle biology and disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1686-y) contains supplementary material, which is available to authorized users.

As data elucidating the complexity of spinal cord injury pathophysiology emerge, it is increasingly being recognized that successful repair will probably require a multifaceted approach that combines tactics from various biomedical disciplines, including pharmacology, cell transplantation, gene therapy and material sciences. Recently, new evidence highlighting the benefit of physical activity and rehabilitation interventions during the post-injury phase has provided novel possibilities in realizing effective repair after spinal cord injury. However, before a comprehensive therapeutic strategy that optimally utilizes the benefits of each of these disciplines can be designed, the basic mechanisms by which these various interventions act must be thoroughly explored and important synergistic and antagonistic interactions identified. In examining the mechanisms by which physical activity-based functional recovery after spinal cord injury is effected, endogenous neural stem cells, in our opinion, engender a potentially key role. Multipotent neural stem cells possess many faculties that abet recovery, including the ability to assess the local microenvironment and deliver biofactors that promote neuroplasticity and regeneration, as well as the potential to replenish damaged or eradicated cellular elements. Encouragingly, the functional recovery owing to physical activity-based therapies appears relatively robust, even when therapy is initiated in the chronic stage of spinal cord injury. In this article, we review experimental outcomes related to our hypothesis that endogenous neural stem cells mediate the functional recovery noted in spinal cord injury following physical activity-based treatments. Overall, the data advocates the incorporation of increased physical activity as a component of the multidimensional treatment of spinal cord injury and underscores the critical need to employ research-based mechanistic approaches for developing future advances in the rehabilitation of neurological injury and disorders.

Bidirectional oscillatory flow (oscillatory shear stress [OSS]: 0.1 ± 3 dyne·cm at 1 Hz) upregulated VEGFR-dependent PKCɛ expression to a greater degree than did unidirectional pulsatile flow (pulsatile shear stress [PSS]: 23 ± 8 dyne·cm at 1 Hz) in human aortic endothelial cells (p < 0.05, n = 3). PSS and OSS further upregulated PKCɛ-dependent PFKFB3 expression for glycolysis (p < 0.05, n = 4). Constitutively active PKCɛ increased, whereas dominant-negative PKCɛ reduced both basal and maximal extracellular acidification rates for glycolytic flux (p < 0.01, n = 4). Metabolomic analysis demonstrated an increase in PKCɛ-dependent glycolytic metabolite, dihydroxyacetone (DHA), but a decrease in gluconeogenic metabolite, aspartic acid (p < 0.05 vs. control, n = 6). In a New Zealand White rabbit model, both PKCɛ and PFKFB3 immunostaining was prominent in the PSS- and OSS-exposed aortic arch and descending aorta. In a transgenic Tg(flk-1:EGFP) zebrafish model, GATA-1a morpholino oligonucleotide injection (to reduce viscosity-dependent shear stress) impaired vascular regeneration after tail amputation (p < 0.01, n = 20), which was restored with PKCɛ messenger RNA (mRNA) rescue (p < 0.05, n = 5). As a corollary, siPKCɛ inhibited tube formation and vascular repair, which were restored by DHA treatment in our Matrigel and zebrafish models. Innovation and Conclusion: Flow-sensitive VEGFR-PKCɛ-PFKFB3 signaling increases the glycolytic metabolite, dihydroxyacetone, to promote vascular repair. Antioxid. Redox Signal. 28, 31-43.

Maintenance of connective tissue integrity is fundamental to sustain function, requiring protein turnover to repair damaged tissue. However, connective tissue proteome dynamics remain largely undefined, as do differences in turnover rates of individual proteins in the collagen and glycoprotein phases of connective tissue extracellular matrix (ECM). Here, we investigate proteome dynamics in the collagen and glycoprotein phases of connective tissues by exploiting the spatially distinct fascicular (collagen-rich) and interfascicular (glycoprotein-rich) ECM phases of tendon. Using isotope labelling, mass spectrometry and bioinformatics, we calculate turnover rates of individual proteins within rat Achilles tendon and its ECM phases. Our results demonstrate complex proteome dynamics in tendon, with ~1000 fold differences in protein turnover rates, and overall faster protein turnover within the glycoprotein-rich interfascicular matrix compared to the collagen-rich fascicular matrix. These data provide insights into the complexity of proteome dynamics in tendon, likely required to maintain tissue homeostasis.

Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries. By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological interactions and networks that were previously unidentifiable. However, to effectively perform integrative analysis of temporal multi-omics, computational methods must account for the heterogeneity and complexity in the data. To this end, we performed unsupervised classification of proteins and metabolites in mice during cardiac remodeling using two innovative deep learning (DL) approaches. First, long short-term memory (LSTM)-based variational autoencoder (LSTM-VAE) was trained on time-series numeric data. The low-dimensional embeddings extracted from LSTM-VAE were then used for clustering. Second, deep convolutional embedded clustering (DCEC) was applied on images of temporal trends. Instead of a two-step procedure, DCEC performes a joint optimization for image reconstruction and cluster assignment. Additionally, we performed K-means clustering, partitioning around medoids (PAM), and hierarchical clustering. Pathway enrichment analysis using the Reactome knowledgebase demonstrated that DL methods yielded higher numbers of significant biological pathways than conventional clustering algorithms. In particular, DCEC resulted in the highest number of enriched pathways, suggesting the strength of its unified framework based on visual similarities. Overall, unsupervised DL is shown to be a promising analytical approach for integrative analysis of temporal multi-omics.