Integrative Biology最新文献

Bipolar disorder (BP) is a lifelong psychiatric condition, which often disrupts the daily life of the patients. It is characterized by unstable and periodic mood changes, which cause patients to display unusual shifts in mood, energy, activity levels, concentration and the ability to carry out day-to-day tasks. BP is a major psychiatric condition, and it is still undertreated. The causes and neural mechanisms of bipolar disorder are unclear, and diagnosis is still mostly based on psychiatric examination, furthermore the unstable character of the disorder makes diagnosis challenging. Identification of the molecular mechanisms underlying the disease may improve the diagnosis and treatment rates. Single nucleotide polymorphisms (SNP) and transcriptome profiles of patients were studied along with signalling pathways that are thought to be associated with bipolar disorder. Here, we present a computational approach that uses publicly available transcriptome data from bipolar disorder patients and healthy controls. Along with statistical analyses, data are integrated with a genome-scale metabolic model and protein-protein interaction network. Healthy individuals and bipolar disorder patients are compared based on their metabolic profiles. We hypothesize that energy metabolism alterations in bipolar disorder relate to perturbations in amino-acid metabolism and neuron-astrocyte exchange reactions. Due to changes in amino acid metabolism, neurotransmitters and their secretion from neurons and metabolic exchange pathways between neurons and astrocytes such as the glutamine-glutamate cycle are also altered. Changes in negatively charged (-1) KIV and KMV molecules are also observed, and it indicates that charge balance in the brain is highly altered in bipolar disorder. Due to this fact, we also hypothesize that positively charged lithium ions may stabilize the disturbed charge balance in neurons in addition to its effects on neurotransmission. To the best of our knowledge, our approach is unique as it is the first study using genome-scale metabolic models in neuropsychiatric research.

The Gram-positive bacterium Bacillus subtilis produces a diverse range of secondary metabolites with different structures and activities. Among them, bacilysin is an enzymatically synthesized dipeptide that consists of L-alanine and L-anticapsin. Previous research by our group has suggested bacilysin's role as a pleiotropic molecule in its producer, B. subtilis PY79. However, the nature of protein interactions in the absence of bacilysin has not been defined. In the present work, we constructed a protein-protein interaction subnetwork by using Omics Integrator based on our recent comparative proteomics data obtained from a bacilysin-silenced strain, OGU1. Functional enrichment analyses on the resulting networks pointed to certain putatively perturbed pathways such as citrate cycle, quorum sensing and secondary metabolite biosynthesis. Various molecules, which were absent from the experimental data, were included in the final network. We believe that this study can guide further experiments in the identification and confirmation of protein-protein interactions in B. subtilis.

Systems biology analysis of intracellular signaling networks has tremendously expanded our understanding of normal and diseased cell behaviors and has revealed paths to finding proper therapeutic molecular targets. When it comes to neurons in the human brain, analysis of intraneuronal signaling networks provides invaluable information on learning, memory and cognition-related disorders, as well as potential therapeutic targets. However, neurons in the human brain form a highly complex neural network that, among its many roles, is also responsible for learning, memory formation and cognition. Given the impairment of these processes in mental and psychiatric disorders, one can envision that analyzing interneuronal processes, together with analyzing intraneuronal signaling networks, can result in a better understanding of the pathology and, subsequently, more effective target discovery. In this paper, a hybrid model is introduced, composed of the long-term potentiation (LTP) interneuronal process and an intraneuronal signaling network regulating CREB. LTP refers to an increased synaptic strength over a long period of time among neurons, typically induced upon occurring an activity that generates high-frequency stimulations (HFS) in the brain, and CREB is a transcription factor known to be highly involved in important functions of the cognitive and executive human brain such as learning and memory. The hybrid LTP-signaling model is analyzed using a proposed molecular fault diagnosis method. It allows to study the importance of various signaling molecules according to how much they affect an intercellular phenomenon when they are faulty, i.e. dysfunctional. This paper is intended to suggest another angle for understanding the pathology and therapeutic target discovery by classifying and ranking various intraneuronal signaling molecules based on how much their faulty behaviors affect an interneuronal process. Possible relations between the introduced hybrid analysis and the previous purely intracellular analysis are investigated in the paper as well.

Cancer is a highly fatal disease without effective early-stage diagnosis and proper treatment. Along with the oncoproteins and oncometabolites, several organelles from cancerous cells are also emerging as potential biomarkers. Mitochondria isolated from cancer cells are one such biomarker candidates. Cancerous mitochondria exhibit different profiles compared with normal ones in morphology, genomic, transcriptomic, proteomic and metabolic landscape. Here, the possibilities of exploring such characteristics as potential biomarkers through single-cell omics and Artificial Intelligence (AI) are discussed. Furthermore, the prospects of exploiting the biomarker-based diagnosis and its futuristic utilization through circulatory tumor cell technology are analyzed. A successful alliance of circulatory tumor cell isolation protocols and a single-cell omics platform can emerge as a next-generation diagnosis and personalized treatment procedure.

The brain microvascular endothelial cells (ECs) play an important role in protecting the brain from hazardous pathogens. However, some viral pathogens can smartly modulate the endothelial pathways to gain entry inside the brain. Further, these viruses can cause endothelial dysfunction which could develop serious neurological ailments. Epstein-Barr virus (EBV), an oncogenic virus, has also been linked to various neurological disorders. The virus primarily infects epithelial and B cells, however, it also has a tendency to infect ECs and cause endothelial activation. However, the impact of EBV influence on ECs is still underexplored. Studying the early events of virus-mediated cellular modulation could help in understanding the virus' infection strategy or aftermath. Raman microspectroscopy has been widely utilized in biomedical sciences to decipher cellular changes. To understand the EBV-influenced EC modulation by studying intracellular biomolecular changes at early time points, we utilized the Raman microspectroscopy tool. We treated the ECs with EBV and acquired the Raman spectra at different time points (2, 4, 6, 12, 24 and 36 h) and different sites (nucleus and periphery) to check changes in Raman intensities associated with specific biomolecules. In the EBV-treated cells, the status of various biomolecules in terms of Raman intensities was observed to be altered compared with uninfected cells. Specifically, the cholesterol, polysaccharide, nucleotides, nucleic acid and proline moieties were altered at different time points. We also investigated the possible correlation between these molecules using molecular network analysis and observed various associated factors. These factors could be influenced by EBV to alter the associated biomolecular levels. Our study paves the pathway to study EBV infection in human brain microvascular ECs and highlights specific biomolecular alterations, which can be focused for further mechanistic investigations.

Macrophages are white blood cells that play disparate roles in homeostasis and immune responses. They can reprogram their phenotypes to pro-inflammatory (M1) or anti-inflammatory (M2) states in response to their environment. About 8-15% of the macrophage transcriptome has circadian oscillations, including genes closely related to their functioning. As circadian rhythms are associated with cellular phenotypes, we hypothesized that polarization of macrophages to opposing subtypes might differently affect their circadian rhythms. We tracked circadian rhythms in RAW 264.7 macrophages using luminescent reporters. Cells were stably transfected with Bmal1:luc and Per2:luc reporters, representing positive and negative components of the molecular clock. Strength of rhythmicity, periods and amplitudes of time series were assessed using multiple approaches. M1 polarization decreased amplitudes and rhythmicities of Bmal1:luc and Per2:luc, but did not significantly affect periods, while M2 polarization increased periods but caused no substantial alterations to amplitudes or rhythmicity. As macrophage phenotypes are also altered in the presence of cancer cells, we tested circadian effects of conditioned media from mouse breast cancer cells. Media from highly aggressive 4T1 cells caused loss of rhythmicity, while media from less aggressive EMT6 cells yielded no changes. As macrophages play roles in tumors, and oncogenic features are associated with circadian rhythms, we tested whether conditioned media from macrophages could alter circadian rhythms of cancer cells. Conditioned media from RAW 264.7 cells resulted in lower rhythmicities and periods, but higher amplitudes in human osteosarcoma, U2OS-Per2:luc cells. We show that phenotypic changes in macrophages result in altered circadian characteristics and suggest that there is an association between circadian rhythms and macrophage polarization state. Additionally, our data demonstrate that macrophages treated with breast cancer-conditioned media have circadian phenotypes similar to those of the M1 subtype, and cancer cells treated with macrophage-conditioned media have circadian alterations, providing insight to another level of cross-talk between macrophages and cancer.

Heterogeneity among cancer cells and in the tumor microenvironment (TME) is thought to be a significant contributor to the heterogeneity of clinical therapy response observed between patients and can evolve over time. A primary example of this is multiple myeloma (MM), a generally incurable cancer where such heterogeneity contributes to the persistent evolution of drug resistance. However, there is a paucity of functional assays for studying this heterogeneity in patient samples or for assessing the influence of the patient TME on therapy response. Indeed, the population-averaged data provided by traditional drug response assays and the large number of cells required for screening remain significant hurdles to advancement. To address these hurdles, we developed a suite of accessible technologies for quantifying functional drug response to a panel of therapies in ex vivo three-dimensional culture using small quantities of a patient's own cancer and TME components. This suite includes tools for label-free single-cell identification and quantification of both cell division and death events with a standard brightfield microscope, an open-source software package for objective image analysis and feasible data management of multi-day timelapse experiments, and a new approach to fluorescent detection of cell death that is compatible with long-term imaging of primary cells. These new tools and capabilities are used to enable sensitive, objective, functional characterization of primary MM cell therapy response in the presence of TME components, laying the foundation for future studies and efforts to enable predictive assessment drug efficacy for individual patients.

This paper describes a microscale fibroplasia and contraction model that is based on fibrin-embedded lung fibroblasts and provides a convenient visual readout of fibrosis. Cell-laden fibrin microgel drops are formed by aqueous two-phase microprinting. The cells deposit extracellular matrix (ECM) molecules such as collagen while fibrin is gradually degraded. Ultimately, the cells contract the collagen-rich matrix to form a compact cell-ECM spheroid. The size of the spheroid provides the visual readout of the extent of fibroplasia. Stimulation of this wound-healing model with the profibrotic cytokine TGF-β1 leads to an excessive scar formation response that manifests as increased collagen production and larger cell-ECM spheroids. Addition of drugs also shifted the scarring profile: the FDA-approved fibrosis drugs (nintedanib and pirfenidone) and a PAI-1 inhibitor (TM5275) significantly reduced cell-ECM spheroid size. Not only is the assay useful for evaluation of antifibrotic drug effects, it is relatively sensitive; one of the few in vitro fibroplasia assays that can detect pirfenidone effects at submillimolar concentrations. Although this paper focuses on lung fibrosis, the approach opens opportunities for studying a broad range of fibrotic diseases and for evaluating antifibrotic therapeutics.