The abnormal accumulation of fat in the liver is often related either to metabolic risk factors associated with metabolic syndrome in the absence of alcohol consumption (nonalcoholic fatty liver disease, NAFLD) or to chronic alcohol consumption (alcoholic fatty liver disease, AFLD). Clinical and histological studies suggest that NAFLD and AFLD share pathogenic mechanisms. Nevertheless, current data are still inconclusive as to whether the underlying biological process and disease pathways of NAFLD and AFLD are alike. Our primary aim was to integrate omics and physiological data to answer the question of whether NAFLD and AFLD share molecular processes that lead to disease development. We also explored the extent to which insulin resistance (IR) is a distinctive feature of NAFLD. To answer these questions, we used systems biology approaches, such as gene enrichment analysis, protein–protein interaction networks, and gene prioritization, based on multi-level data extracted by computational data mining. We observed that the leading disease pathways associated with NAFLD did not significantly differ from those of AFLD. However, systems biology revealed the importance of each molecular process behind each of the two diseases, and dissected distinctive molecular NAFLD and AFLD-signatures. Comparative co-analysis of NAFLD and AFLD clarified the participation of NAFLD, but not AFLD, in cardiovascular disease, and showed that insulin signaling is impaired in fatty liver regardless of the noxa, but the putative regulatory mechanisms associated with NAFLD seem to encompass a complex network of genes and proteins, plausible of epigenetic modifications. Gene prioritization showed a cancer-related functional map that suggests that the fatty transformation of the liver tissue is regardless of the cause, an emerging mechanism of ubiquitous oncogenic activation. In conclusion, similar underlying disease mechanisms lead to NAFLD and AFLD, but specific ones depict a particular disease signature that has a different impact on the systemic context.
The recent advent of high-throughput microarray data has enabled the global analysis of the transcriptome, driving the development and application of computational approaches to study transcriptional regulation on the genome scale, by reconstructing in silico the regulatory interactions of the gene network. Although there are many in-depth reviews of such ‘reverse-engineering’ methodologies, most have focused on the practical aspect of data mining, and few on the biological problem and the biological relevance of the methodology. Therefore, in this review, from a biological perspective, we used a set of yeast microarray data as a working example, to evaluate the fundamental assumptions implicit in associating transcription factor (TF)–target gene expression levels and estimating TFs’ activity, and further explore cooperative models. Finally we confirm that the detailed transcription mechanism is overly-complex for expression data alone to reveal, nevertheless, future network reconstruction studies could benefit from the incorporation of context-specific information, the modeling of multiple layers of regulation (e.g. micro-RNA), or the development of approaches for context-dependent analysis, to uncover the mechanisms of gene regulation.
transcription factors; transcriptional regulation; network reconstruction; gene expression
Background. This cross-sectional study examined the effect of aging on performing finger-pointing tasks involving choices and whether experienced older Tai Chi practitioners perform better than healthy older controls in such tasks. Methods. Thirty students and 30 healthy older controls were compared with 31 Tai Chi practitioners. All the subjects performed a rapid index finger-pointing task. The visual signal appeared randomly under 3 conditions: (1) to touch a black ball as quickly and as accurately as possible, (2) not to touch a white ball, (3) to touch only the white ball when a black and a white ball appeared simultaneously. Reaction time (RT) of anterior deltoid electromyogram, movement time (MT) from electromyogram onset to touching of the target, end-point accuracy from the center of the target, and the number of wrong movements were recorded. Results. Young students displayed significantly faster RT and MT, achieving significantly greater end-point accuracy and fewer wrong movements than older controls. Older Tai Chi practitioners had significantly faster MT than older controls. Conclusion. Finger-pointing tasks with a choice paradigm became slower and less accurate with age. Positive findings suggest that Tai Chi may slow down the aging effect on eye-hand coordination tasks involving choices that require more cognitive progressing.
Liver metabolism is altered after systemic injuries such as burns and trauma. These changes have been elucidated in rat models of experimental burn injury where the liver was isolated and perfused ex vivo. Because these studies were performed in fasted animals to deplete glycogen stores, thus simplifying quantification of gluconeogenesis, these observations reflect the combined impact of fasting and injury on liver metabolism. Herein we asked whether the metabolic response to experimental burn injury is different in fed vs. fasted animals. Rats were subjected to a cutaneous burn covering 20% of the total body surface area, or to similar procedures without administering the burn, hence a sham-burn. Half of the animals in the burn and sham-burn groups were fasted starting on postburn day 3, and the others allowed to continue ad libitum. On postburn day 4, livers were isolated and perfused for 1 hour in physiological medium supplemented with 10% hematocrit red blood cells. The uptake/release rates of major carbon and nitrogen sources, oxygen, and carbon dioxide were measured during the perfusion and the data fed into a mass balance model to estimate intracellular fluxes. The data show that in fed animals, injury increased glucose output mainly from glycogen breakdown and minimally impacted amino acid metabolism. In fasted animals, injury did not increase glucose output but increased urea production and the uptake of several amino acids, namely glutamine, arginine, glycine, and methionine. Furthermore, sham-burn animals responded to fasting by triggering gluconeogenesis from lactate; however, in burned animals the preferred gluconeogenic substrate was amino acids. Taken together, these results suggest that the fed state prevents the burn-induced increase in hepatic amino acid utilization for gluconeogenesis. The role of glycogen stores and means to increase and/or maintain internal sources of glucose to prevent increased hepatic amino acid utilization warrant further studies.
Shannon’s theory of communication has been very successfully applied for the analysis of biological information. However, the theory neglects semantic and pragmatic aspects and thus cannot directly be applied to distinguish between (bio-) chemical systems able to process “meaningful” information from those that do not. Here, we present a formal method to assess a system’s semantic capacity by analyzing a reaction network’s capability to implement molecular codes. We analyzed models of chemical systems (martian atmosphere chemistry and various combustion chemistries), biochemical systems (gene expression, gene translation, and phosphorylation signaling cascades), an artificial chemistry, and random reaction networks. Our study suggests that different chemical systems posses different semantic capacities. No semantic capacity was found in the model of the martian atmosphere chemistry, the studied combustion chemistries, and highly connected random networks, i.e. with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the studied biochemical systems and in random reaction networks where the number of second order reactions is twice the number of species. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g. in the context of the origin of life.
Palmitic acid, the most common saturated free fatty acid, has been implicated in ER (endoplasmic reticulum) stress-mediated apoptosis. This lipoapotosis is dependent, in part, on the upregulation of the activating transcription factor-4 (ATF4). To better understand the mechanisms by which palmitate upregulates the expression level of ATF4, we integrated literature information on palmitate-induced ER stress signaling into a discrete dynamic model. The model provides an in silico framework that enables simulations and predictions. The model predictions were confirmed through further experiments in human hepatocellular carcinoma (HepG2) cells and the results were used to update the model and our current understanding of the signaling induced by palmitate.
The three key things from the in silico simulation and experimental results are: 1) palmitate induces different signaling pathways (PKR (double-stranded RNA-activated protein kinase), PERK (PKR-like ER kinase), PKA (cyclic AMP (cAMP)-dependent protein kinase A) in a time dependent-manner, 2) both ATF4 and CREB1 (cAMP-responsive element-binding protein 1) interact with the Atf4 promoter to contribute to a prolonged accumulation of ATF4, and 3) CREB1 is involved in ER-stress induced apoptosis upon palmitate treatment, by regulating ATF4 expression and possibly Ca2+ dependent-CaM (calmodulin) signaling pathway.
The in silico model helped to delineate the essential signaling pathways in palmitate-mediated apoptosis.
ATF4; Palmitate-induced ER stress; CREB1; Discrete dynamic model; Signal transduction
It is clinically important to be able to detect influenza A/H1N1 virus using a fast, portable, and accurate system that has high specificity and sensitivity. To achieve this goal, it is necessary to develop a highly specific primer set that recognizes only influenza A viral genes and a rapid real-time PCR system that can detect even a single copy of the viral gene. In this study, we developed and validated a novel fluidic chip-type real-time PCR (LabChip real-time PCR) system that is sensitive and specific for the detection of influenza A/H1N1, including the pandemic influenza strain A/H1N1 of 2009. This LabChip real-time PCR system has several remarkable features: (1) It allows rapid quantitative analysis, requiring only 15 min to perform 30 cycles of real-time PCR. (2) It is portable, with a weight of only 5.5 kg. (3) The reaction cost is low, since it uses disposable plastic chips. (4) Its high efficiency is equivalent to that of commercially available tube-type real-time PCR systems. The developed disposable LabChip is an economic, heat-transferable, light-transparent, and easy-to-fabricate polymeric chip compared to conventional silicon- or glass-based labchip. In addition, our LabChip has large surface-to-volume ratios in micro channels that are required for overcoming time consumed for temperature control during real-time PCR. The efficiency of the LabChip real-time PCR system was confirmed using novel primer sets specifically targeted to the hemagglutinin (HA) gene of influenza A/H1N1 and clinical specimens. Eighty-five human clinical swab samples were tested using the LabChip real-time PCR. The results demonstrated 100% sensitivity and specificity, showing 72 positive and 13 negative cases. These results were identical to those from a tube-type real-time PCR system. This indicates that the novel LabChip real-time PCR may be an ultra-fast, quantitative, point-of-care-potential diagnostic tool for influenza A/H1N1 with a high sensitivity and specificity.
A fundamental issue in biology and medicine is illustration of the overall drug impact which is always the consequence of changes in local regions of metabolic pathways (subpathways). To gain insights into the global relationship between drugs and their affected metabolic subpathways, we constructed a drug–metabolic subpathway network (DRSN). This network included 3925 significant drug–metabolic subpathway associations representing drug dual effects. Through analyses based on network biology, we found that if drugs were linked to the same subpathways in the DRSN, they tended to share the same indications and side effects. Furthermore, if drugs shared more subpathways, they tended to share more side effects. We then calculated the association score by integrating drug-affected subpathways and disease-related subpathways to quantify the extent of the associations between each drug class and disease class. The results showed some close drug–disease associations such as sex hormone drugs and cancer suggesting drug dual effects. Surprisingly, most drugs displayed close associations with their side effects rather than their indications. To further investigate the mechanism of drug dual effects, we classified all the subpathways in the DRSN into therapeutic and non-therapeutic subpathways representing drug therapeutic effects and side effects. Compared to drug side effects, the therapeutic effects tended to work through tissue-specific genes and these genes tend to be expressed in the adrenal gland, liver and kidney; while drug side effects always occurred in the liver, bone marrow and trachea. Taken together, the DRSN could provide great insights into understanding the global relationship between drugs and metabolic subpathways.
Polyelectrolyte multilayer (PEM) transfer printing has been previously achieved by stamping under dry conditions. Here, we show for the first time, that PEM can be transferred from a stamp to the base substrate under aqueous conditions whereby the two surfaces are in a non-contact mode. Degradable multilayers of (PAA/PEG)10.5 followed by non-degradable multilayers of (PDAC/SPS)80.5 were fabricated under acidic pH conditions on either PDMS or glass (stamp), and subsequently transferred over top of another multilayer prepared on a different substrate (base substrate), with a spacing of ~ 200 μm between the stamping surface and the base substrate. This multilayer transfer was performed under physiological pH conditions. This process is referred to herein as non-contact, aqueous-phase multilayer (NAM) transfer. NAM transfer can be useful for applications such as fabricating three-dimensional (3-D) cellular scaffolds. We attempted to create a 3-D cellular scaffold using NAM transfer, and characterized the scaffolds with conventional and fluorescence microscopy.
polyelectrolyte multilayer transfer; non-contact stamping; aqueous phase stamping; cellular scaffold
This work presents an adapted Random Sampling - High Dimensional Model Representation (RS-HDMR) algorithm for synergistically addressing three key problems in network biology: (1) identifying the structure of biological networks from multivariate data, (2) predicting network response under previously unsampled conditions, and (3) inferring experimental perturbations based on the observed network state. RS-HDMR is a multivariate regression method that decomposes network interactions into a hierarchy of non-linear component functions. Sensitivity analysis based on these functions provides a clear physical and statistical interpretation of the underlying network structure. The advantages of RS-HDMR include efficient extraction of nonlinear and cooperative network relationships without resorting to discretization, prediction of network behavior without mechanistic modeling, robustness to data noise, and favorable scalability of the sampling requirement with respect to network size. As a proof-of-principle study, RS-HDMR was applied to experimental data measuring the single-cell response of a protein-protein signaling network to various experimental perturbations. A comparison to network structure identified in the literature and through other inference methods, including Bayesian and mutual-information based algorithms, suggests that RS-HDMR can successfully reveal a network structure with a low false positive rate while still capturing non-linear and cooperative interactions. RS-HDMR identified several higher-order network interactions that correspond to known feedback regulations among multiple network species and that were unidentified by other network inference methods. Furthermore, RS-HDMR has a better ability to predict network response under unsampled conditions in this application than the best statistical inference algorithm presented in the recent DREAM3 signaling-prediction competition. RS-HDMR can discern and predict differences in network state that arise from sources ranging from intrinsic cell-cell variability to altered experimental conditions, such as when drug perturbations are introduced. This ability ultimately allows RS-HDMR to accurately classify the experimental conditions of a given sample based on its observed network state.
Establishing causative links between protein functional domains and global gene regulation is critical for advancements in genetics, biotechnology, disease treatment, and systems biology. This task is challenging for multifunctional proteins when relying on traditional approaches such as gene deletions since they remove all domains simultaneously. Here, we describe a novel approach to extract quantitative, causative links by modulating the expression of a dominant mutant allele to create a function-specific competitive inhibition. Using the yeast histone acetyltransferase Gcn5p as a case study, we demonstrate the utility of this approach and (1) find evidence that Gcn5p is more involved in cell-wide gene repression, instead of the accepted gene activation associated with HATs, (2) identify previously unknown gene targets and interactions for Gcn5p-based acetylation, (3) quantify the strength of some Gcn5p-DNA associations, (4) demonstrate that this approach can be used to correctly identify canonical chromatin modifications, (5) establish the role of acetyltransferase activity on synthetic lethal interactions, and (6) identify new functional classes of genes regulated by Gcn5p acetyltransferase activity—all six of these major conclusions were unattainable by using standard gene knockout studies alone. We recommend that a graded dominant mutant approach be utilized in conjunction with a traditional knockout to study multifunctional proteins and generate higher-resolution data that more accurately probes protein domain function and influence.
The contribution of mutations in amyloid precursor protein (APP) and presenilin (PSEN) to familial Alzheimer's disease (AD) is well established. However, little is known about the molecular mechanisms leading to amyloid beta (Aβ) generation in sporadic AD. Increased brain ceramide levels have been associated with sporadic AD, and are a suggested risk factor. Serine palmitoyltransferase (SPT) is the first rate limiting enzyme in the de novo ceramide synthesis. However, the regulation of SPT is not yet understood. Evidence suggests that it may be post-transcriptionally regulated. Therefore, we investigated the role of miRNAs in the regulation of SPT and amyloid beta (Aβ) generation. We show that serine palmitoyltransferase (SPT) is upregulated in a subgroup of sporadic AD patient brains. This is further confirmed in mouse model studies of risk factors associated with AD. We identified that the loss of miR-137, -181c, -9 and 29a/b-1 increases SPT and in turn Aβ levels, and provides a mechanism for the elevated risk of AD associated with age, high saturated fat diet and gender. Finally, these results suggest SPT and the respective miRNAs may be potential therapeutic targets for sporadic AD.
Alzheimer's disease; amyloid beta; serine palmitoyltransferase; microRNA
Colorectal cancer (CRC) has one of the highest incidences among all cancers. The majority of CRCs are sporadic cancers that occur in individuals without family histories of CRC or inherited mutations. Unfortunately, whole-genome expression studies of sporadic CRCs are limited. A recent study used microarray techniques to identify a predictor gene set indicative of susceptibility to early-onset CRC. However, the molecular mechanisms of the predictor gene set were not fully investigated in the previous study. To understand the functional roles of the predictor gene set, in the present study we applied a subpathway-based statistical model to the microarray data from the previous study and identified mechanisms that are reasonably associated with the predictor gene set. Interestingly, significant subpathways belonging to 2 KEGG pathways (focal adhesion; natural killer cell-mediated cytotoxicity) were found to be involved in the early-onset CRC patients. We also showed that the 2 pathways were functionally involved in the predictor gene set using a text-mining technique. Entry of a single member of the predictor gene set triggered a focal adhesion pathway, which confers anti-apoptosis in the early-onset CRC patients. Furthermore, intensive inspection of the predictor gene set in terms of the 2 pathways suggested that some entries of the predictor gene set were implicated in immunosuppression along with epithelial-mesenchymal transition (EMT) in the early-onset CRC patients. In addition, we compared our subpathway-based statistical model with a gene set-based statistical model, MIT Gene Set Enrichment Analysis (GSEA). Our method showed better performance than GSEA in the sense that our method was more consistent with a well-known cancer-related pathway set. Thus, the biological suggestion generated by our subpathway-based approach seems quite reasonable and warrants a further experimental study on early-onset CRC in terms of dedifferentiation or differentiation, which is underscored in EMT and immunosuppression.
Cell replacement using stem cells is a promising therapeutic approach to treat degenerative motor neuron (MN) disorders, such as amyotrophic lateral sclerosis and spinal cord injury. Human bone marrow-derived mesenchymal stem cells (hMSCs) are a desirable cell source for autologous cell replacement therapy to treat nervous system injury due to their plasticity, low immunogenicity, and a lower risk of tumor formation than embryonic stem cells. However, hMSCs are inefficient with regards to differentiating into MN-like cells. To solve this limitation, we genetically engineered hMSCs to express MN-associated transcription factors, Olig2 and Hb9, and then treat the hMSCs expressing Olig2 and Hb9 with optimal MN induction medium (MNIM). This method of induction led to higher expression (>30% of total cells) of MN markers. Electrophysiological data revealed that the induced hMSCs had the excitable properties of neurons and were able to form functional connections with muscle fibers in vitro. Furthermore, when the induced hMSCs were transplanted into an injured organotypic rat spinal cord slice culture, an ex vivo model of spinal cord injury, they exhibited characteristics of MNs. The data strongly suggest that induced Olig2/Hb9-expressing hMSCs were clearly reprogrammed and directed toward a MN-like lineage. We propose that methods to induce Olig2 and Hb9, followed by further induction with MNIM have therapeutic potential for autologous cell replacement therapy to treat degenerative MN disorders.
The intracellular second messenger cAMP is frequently used in induction media to induce mesenchymal stem cells (MSCs) into neural lineage cells. To date, an understanding of the role cAMP exerts on MSCs and whether cAMP can induce MSCs into functional neurons is still lacking. We found cAMP initiated neuron-like morphology changes early and neural differentiation much later. The early phase changes in morphology were due to cell shrinkage, which subsequently rendered some cells apoptotic. While the morphology changes occurred prior to the expression of neural markers, it is not required for neural marker expression and the two processes are differentially regulated downstream of cAMP-activated protein kinase A. cAMP enabled MSCs to gain neural marker expressions with neuronal function, such as, calcium rise in response to neuronal activators, dopamine, glutamate, and potassium chloride. However, only some of the cells induced by cAMP responded to the three neuronal activators and further lack the neuronal morphology, suggesting that although cAMP is able to direct MSCs towards neural differentiation, they do not achieve terminal differentiation.
cAMP; Morphology; Apoptosis; Neural differentiation
Endothelial cells (ECs) are continuously exposed to hemodynamic forces imparted by blood flow. While it is known that endothelial behavior can be influenced by cytokine activation or fluid shear, the combined effects of these two independent agonists have yet to be fully elucidated.
We investigated EC response to long-term inflammatory cues under physiologically relevant shear conditions via E-selectin expression where monolayers of human umbilical vein ECs were simultaneously exposed to laminar fluid shear and interleukin-1ß (shear-cytokine activation) in a parallel plate flow chamber.
Results and Conclusion
Naïve ECs exposed to shear-cytokine activation display significantly higher E-selectin expression for up to 24 hr relative to ECs activated in static (static-cytokine). Peak E-selectin expression occurred after 8–12 hr of continuous shear-cytokine activation contrary to the commonly observed 4–6 hr peak expression in ECs exposed to static-cytokine activation. Cells with some history of high shear conditioning exhibited either high or muted E-selectin expression depending on the durations of the shear pre-conditioning and the ensuing shear-cytokine activation. Overall, the presented data suggest that a high laminar shear enhances acute EC response to interleukin-1ß in naïve or shear-conditioned ECs as may be found in the pathological setting of ischemia/reperfusion injury while conferring rapid E-selectin downregulation to protect against chronic inflammation.
PKR (double-stranded RNA-activated protein kinase) is an important component of the innate immunity, antiviral and apoptotic pathways. Recently, our group found that palmitate, a saturated fatty acid, is involved in apoptosis by reducing the autophosphorylation of PKR at the Thr451 residue, however, the molecular mechanism by which palmitate reduces PKR autophosphorylation is not known. Thus, we investigated how palmitate affects the phosphorylation of the PKR protein at the molecular and biophysical levels. Biochemical and computational studies show that palmitate binds to PKR, near the ATP-binding site, thereby inhibiting its autophosphorylation at Thr451 and Thr446. Mutation studies suggests that Lys296 and Asp432 in the ATP binding site on the PKR protein are important for palmitate binding. We further confirmed that palmitate also interacts with other kinases, due to the conserved ATP-binding site. A better understanding of how palmitate interacts with the PKR protein, as well as other kinases, could shed light onto possible mechanisms by which palmitate mediates kinase signaling pathways, that could have implications on the efficacy of current drug therapies that target kinases.
The era of personalized medicine for cancer therapeutics has taken an important step forward in making accurate prognoses for individual patients with the adoption of high-throughput microarray technology. However, microarray technology in cancer diagnosis or prognosis has been primarily used for the statistical evaluation of patient populations, and thus excludes inter-individual variability and patient-specific predictions. Here we propose a metric called clinical confidence that serves as a measure of prognostic reliability to facilitate the shift from population-wide to personalized cancer prognosis using microarray-based predictive models. The performance of sample-based models predicted with different clinical confidences was evaluated and compared systematically using three large clinical datasets studying the following cancers: breast cancer, multiple myeloma, and neuroblastoma. Survival curves for patients, with different confidences, were also delineated. The results show that the clinical confidence metric separates patients with different prediction accuracies and survival times. Samples with high clinical confidence were likely to have accurate prognoses from predictive models. Moreover, patients with high clinical confidence would be expected to live for a notably longer or shorter time if their prognosis was good or grim based on the models, respectively. We conclude that clinical confidence could serve as a beneficial metric for personalized cancer prognosis prediction utilizing microarrays. Ascribing a confidence level to prognosis with the clinical confidence metric provides the clinician an objective, personalized basis for decisions, such as choosing the severity of the treatment.
Activation of CaMKII by calmodulin and the subsequent maintenance of constitutive activity through autophosphorylation at threonine residue 286 (Thr286) are thought to play a major role in synaptic plasticity. One of the effects of autophosphorylation at Thr286 is to increase the apparent affinity of CaMKII for calmodulin, a phenomenon known as “calmodulin trapping”. It has previously been suggested that two binding sites for calmodulin exist on CaMKII, with high and low affinities, respectively. We built structural models of calmodulin bound to both of these sites. Molecular dynamics simulation showed that while binding of calmodulin to the supposed low-affinity binding site on CaMKII is compatible with closing (and hence, inactivation) of the kinase, and could even favour it, binding to the high-affinity site is not. Stochastic simulations of a biochemical model showed that the existence of two such binding sites, one of them accessible only in the active, open conformation, would be sufficient to explain calmodulin trapping by CaMKII. We can explain the effect of CaMKII autophosphorylation at Thr286 on calmodulin trapping: It stabilises the active state and therefore makes the high-affinity binding site accessible. Crucially, a model with only one binding site where calmodulin binding and CaMKII inactivation are strictly mutually exclusive cannot reproduce calmodulin trapping. One of the predictions of our study is that calmodulin binding in itself is not sufficient for CaMKII activation, although high-affinity binding of calmodulin is.
Single cell analysis has allowed critical discoveries in drug testing, immunobiology and stem cell research. In addition, a change from two to three dimensional growth conditions radically affects cell behavior. This already resulted in new observations on gene expression and communication networks and in better predictions of cell responses to their environment. However, it is still difficult to study the size and shape of single cells that are freely suspended, where morphological changes are highly significant. Described here is a new method for quantitative real time monitoring of cell size and morphology, on single live suspended cancer cells, unconfined in three dimensions. The precision is comparable to that of the best optical microscopes, but, in contrast, there is no need for confining the cell to the imaging plane. The here first introduced cell magnetorotation (CM) method is made possible by nanoparticle induced cell magnetization. By using a rotating magnetic field, the magnetically labeled cell is actively rotated, and the rotational period is measured in real-time. A change in morphology induces a change in the rotational period of the suspended cell (e.g. when the cell gets bigger it rotates slower). The ability to monitor, in real time, cell swelling or death, at the single cell level, is demonstrated. This method could thus be used for multiplexed real time single cell morphology analysis, with implications for drug testing, drug discovery, genomics and three-dimensional culturing.
Cellular signaling networks display complex architecture. Defining the design principle of this architecture is crucial for our understanding of various biological processes. Using a mathematical model for three-node feed-forward loops, we identify that the organization of motifs in specific manner within the network serves as an important regulator of signal processing. Further, incorporating a systemic stochastic perturbation to the model we could propose a possible design principle, for higher-order organization of motifs into larger networks in order to achieve specific biological output. The design principle was then verified in a large, complex human cancer signaling network. Further analysis permitted us to classify signaling nodes of the network into robust and vulnerable nodes as a result of higher order motif organization. We show that distribution of these nodes within the network at strategic locations then provides for the range of features displayed by the signaling network.
The discovery of RNA interference (RNAi) generated considerable interest in developing short interfering RNAs (siRNAs) for understanding basic biology and as the active agents in a new variety of therapeutics. Early studies showed that selecting an active siRNA was not as straightforward as simply picking a sequence on the target mRNA and synthesizing the siRNA complementary to that sequence. As interest in applying RNAi has increased, the methods for identifying active siRNA sequences have evolved from focusing on the simplicity of synthesis and purification, to identifying preferred target sequences and secondary structures, to predicting the thermodynamic stability of the siRNA. As more specific details of the RNAi mechanism have been defined, these have been incorporated into more complex siRNA selection algorithms, increasing the reliability of selecting active siRNAs against a single target. Ultimately, design of the best siRNA therapeutics will require design of the siRNA itself, in addition to design of the vehicle and other components necessary for it to function in vivo. In this minireview, we will summarize the evolution of siRNA selection techniques with a particular focus on one issue of current importance to the field, how best to identify those siRNA sequences likely to have high activity. Approaches to designing active siRNAs through chemical and structural modifications will also be highlighted. As the understanding of how to control the activity and specificity of siRNAs improves, the potential utility of siRNAs as human therapeutics will concomitantly grow.
The regulation of complex cellular activities in palmitate treated HepG2 cells, and the ensuing cytotoxic phenotype, involves cooperative interactions between genes. While previous approaches have largely focused on identifying individual target genes, elucidating interacting genes has thus far remained elusive. We applied the concept of information synergy to reconstruct a “gene-cooperativity” network for palmititate-induced cytotoxicity in liver cells. Our approach integrated gene expression data with metabolic profiles to select a subset of genes for network reconstruction. Subsequent analysis of the network revealed insulin signaling as the most significantly enriched pathway, and desmoplakin (DSP) as its top neighbor. We determined that palmitate significantly reduces DSP expression, and treatment with insulin restores the lost expression of DSP. Insulin resistance is a common pathological feature of fatty liver and related ailments, whereas loss of DSP has been noted in liver carcinoma. Reduced DSP expression can lead to loss of cell-cell adhesion via desmosomes, and disrupt the keratin intermediate filament network. Our findings suggest that DSP expression may be perturbed by palmitate and, along with insulin resistance, may play a role in palmitate induced cytotoxicity, and serve as potential targets for further studies on non-alcoholic fatty liver disease (NAFLD).
An important feature that emerges from analyzing gene regulatory networks is the "switch-like behavior" or "bistability", a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches plays pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to be able to systematically identify gene switches.
We propose a top-down mining approach to exploring gene switches on a genome-scale level. Theoretical analysis, proof-of-concept examples, and experimental studies demonstrate the ability of our mining approach to identify bistable genes by sampling across a variety of different conditions. Applying the approach to human breast cancer data identified genes that show bimodality within the cancer samples, such as estrogen receptor (ER) and ERBB2, as well as genes that show bimodality between cancer and non-cancer samples, where tumor-associated calcium signal transducer 2 (TACSTD2) is uncovered. We further suggest a likely transcription factor that regulates TACSTD2.
Our mining approach demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network. To the best of our knowledge, this is the first attempt in applying mining approaches to explore gene switches on a genome-scale, and the identification of TACSTD2 demonstrates that single cell-level bistability can be predicted from microarray data. Experimental confirmation of the computational results suggest TACSTD2 could be a potential biomarker and attractive candidate for drug therapy against both ER+ and ER- subtypes of breast cancer, including the triple negative subtype.
Proteoglycans and hyaluronan play critical roles in heart development. In this study, human embryonic stem cells (hESC) were used as a model to quantify the synthesis of proteoglycans and hyaluronan in hESC in the early stages of differentiation, and after directed differentiation into cardiomyocytes. We demonstrated that both hESC and cardiomyocyte cultures synthesize an extracellular matrix (ECM) enriched in proteoglycans and hyaluronan. During cardiomyocyte differentiation, total proteoglycan and hyaluronan decreased and the proportion of proteoglycans bearing heparan sulfate chains was reduced. Versican, a chondroitin sulfate proteoglycan, accumulated in hESC and cardiomyocyte cultures. Furthermore, versican synthesized by hESC contained more N- and O-linked oligosaccharide than versican from cardiomyocytes. Transcripts for the versican variants, V0, V1, V2 and V3, increased in cardiomyocytes compared to hESC, with V1 most abundant. Hyaluronan in hESC had lower molecular weight than hyaluronan from cardiomyocyte cultures. These changes were accompanied by an increase in HAS-1 and HAS-2 mRNA in cardiomyocyte cultures, with HAS-2 most abundant. Interestingly, HAS-3 was absent from the cardiomyocyte cultures, but expressed by hESC. These results indicate that human cardiomyocyte differentiation is accompanied by specific changes in the expression and accumulation of ECM components and suggest a role for versican and hyaluronan in this process.
versican; hyaluronan; cardiomyocyte; differentiation; stem cells; glycosylation