Here we show that glioblastoma express high levels of branched-chain amino acid transaminase 1 (BCAT1), the enzyme that initiates the catabolism of branched-chain amino acids (BCAAs). Expression of ...BCAT1 was exclusive to tumors carrying wild-type isocitrate dehydrogenase 1 (IDH1) and IDH2 genes and was highly correlated with methylation patterns in the BCAT1 promoter region. BCAT1 expression was dependent on the concentration of α-ketoglutarate substrate in glioma cell lines and could be suppressed by ectopic overexpression of mutant IDH1 in immortalized human astrocytes, providing a link between IDH1 function and BCAT1 expression. Suppression of BCAT1 in glioma cell lines blocked the excretion of glutamate and led to reduced proliferation and invasiveness in vitro, as well as significant decreases in tumor growth in a glioblastoma xenograft model. These findings suggest a central role for BCAT1 in glioma pathogenesis, making BCAT1 and BCAA metabolism attractive targets for the development of targeted therapeutic approaches to treat patients with glioblastoma.
The immune response of the critically ill after severe trauma is sex-specific and may explain the different progression of the disease. This may be explained by a different gene regulatory program of ...their peripheral immune cells. We investigated the progression of the transcription profiles of peripheral immune cells of the patients to elucidate their distinct physiological response and clinical course.
We compared transcription profiles of whole blood of male and female patients from a larger longitudinal study of critically ill patients after trauma. We developed a statistical analysis pipeline that synchronized the time lapse of the profiles based on the temporal severity score of each patient.
This enabled to categorize the temporal progression of the disease into two pre-acute, an acute and two post-acute phases. Comparing gene regulation of male and female patients at each phase, we identified distinctively regulated molecular processes mainly in the immune response, but also in the regulation of metabolism allowing to cluster these discriminative gene sets into sets of highly related cellular processes. Compared to male patients and healthy controls, female patients showed upregulation of gene sets of innate immunity in the early phase, upregulation of wound healing processes during the acute phase and upregulation of adaptive immunity in the late phase indicating early recovery. In turn, during the pre-acute and acute phase, male patients showed less suppression of gene sets coding for enzymes of energy metabolism and anabolism, most prominently the tricarboxylic acid cycle and β-oxidation, and cellular maintenance, such as cell cycle, DNA replication and damage response, and RNA metabolism.
A stronger innate immune response at the very early phase of the disease may support early clearance of the pathogen and its associated molecular patterns. Upregulation of wound healing processes may explain reduced multiple organ failure during the acute phase. Down regulated energy metabolism during the acute phase may make female patients less susceptible to oxidative stress, the upregulated adaptive immune system reflects an earlier recovery and rebuilding of the adaptive immune system that may protect them from secondary infections. Follow up studies need to be performed confirming these observations experimentally.
After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome‐scale metabolic models (GEMs). However, it ...is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild‐type and evolved strains. Here, we report that >98% of active reactions from FBA optimal growth solutions are supported by transcriptomic and proteomic data. Moreover, when E. coli adapts to growth rate selective pressure, the evolved strains upregulate genes within the optimal growth predictions, and downregulate genes outside of the optimal growth solutions. In addition, bottlenecks from dosage limitations of computationally predicted essential genes are overcome in the evolved strains. We also identify regulatory processes that may contribute to the development of the optimal growth phenotype in the evolved strains, such as the downregulation of known regulons and stringent response suppression. Thus, differential gene and protein expression from wild‐type and adaptively evolved strains supports observed growth phenotype changes, and is consistent with GEM‐computed optimal growth states.
Synopsis
When prokaryotes are maintained at early‐ to mid‐log phase growth through serial passaging for hundreds of generations, the strains improve fitness and evolve a higher growth rate (Lenski and Travisano, 1994; Ibarra et al, 2002). This increased growth rate is the result of the appearance of a few causal mutations (Herring et al, 2006; Conrad et al, 2009). In Escherichia coli, these altered growth phenotypes are consistent with predictions from genome‐scale models of metabolism (GEMs) (Ibarra et al, 2002; Fong and Palsson, 2004). However, it is still not known (1) whether absolute gene and protein expression levels and expression changes are consistent with optimal growth predictions from in silico GEMs or (2) whether measured expression changes can be linked to physiological changes that are based on known mechanisms or pathways. In this study, we begin to address these questions using constraint‐based modeling of E. coli K‐12 metabolism (Feist and Palsson, 2008) to analyze omic data that document the expression changes in E. coli under adaptive evolution in three different growth conditions.
Mapping high‐throughput data to a network can be useful for interpretation. However, it does not account for upstream and downstream effects of gene and protein expression changes. The analysis of data in the context of GEMs can suggest if predicted activity is consistent with the data. For this work, we used a variant of flux balance analysis (FBA), called Parsimonious enzyme usage FBA (pFBA) (Figure 1), to classify all genes according to whether they are used in the optimal growth solutions. Results from these models were compared with the data to assess whether the data were consistent with genes and proteins within the predicted optimal solutions, and whether the expression changes were consistent with measured physiology. Through this analysis, we find that the data provide a high coverage of genes that contribute to the optimal growth solutions (Figure 1B). In fact, the union of the proteomic and transcriptomic data for non‐essential genes provides support for 97.7% of all non‐essential gene‐associated reactions within the optimal growth predictions. Thus, the spectrum of expressed genes and proteins is consistent with the pathway utilization that is predicted for these optimal growth phenotypes.
Laboratory‐evolved strains attain a higher growth rate. This higher growth rate is usually associated with an increased substrate uptake rate (Ibarra et al, 2002; Fong et al, 2005) and in some cases more efficient metabolism (Ibarra et al, 2002). Both of these properties are also witnessed in the strains studied here. It has been reported that in most cases, evolved strain growth phenotype is consistent with GEM predictions (Ibarra et al, 2002; Teusink et al, 2009). Here, we evaluate whether the laboratory‐evolved strains adjust the gene and protein expression levels in accordance with pathway usage in the optimal growth predictions. Essential and non‐essential genes and proteins within the optimal growth solutions are significantly upregulated (Figure 1B). This suggests that these proteins may be acting as bottlenecks that are relieved through the adaptive process, thereby allowing for a higher substrate uptake rate and growth rate. However, genes and proteins associated with reactions that cannot carry a flux in the given growth conditions are downregulated in the evolved strains (Figure 1B). Furthermore, there is downregulation of genes associated with less efficient pathways (Figure 5C). Thus, the omic data support the emergence of the predicted optimal growth states, consistent with the increased substrate uptake upstream and the increased biomass production downstream of these internal pathways.
Regulatory mechanisms, both known and unknown, are responsible for the changes seen here. Across all data sets, several metabolic regulons are significantly downregulated. However, no known regulons were enriched among upregulated genes or proteins for all but one data set. Aside from just regulating the metabolic pathways directly, these mechanisms lead to additional physiological changes. For example, in the minimal media growth conditions used here, the stringent response normally represses growth while upregulating amino‐acid biosynthetic processes. However, evolved strain gene expression shows a suppression of the stringent response, as evolved strain gene expression shows either no expression change or changes opposite to the normal stringent response.
The implications of this work are as follows: (1) genome‐scale gene and protein expression data are consistent with FBA computed optimal growth states, and evolved strains reinforce these optimal states; (2) genome‐scale models will have an important function bridging the gap between genotype and phenotype; and (3) the development of additional genome‐scale models of other growth‐related processes such as transcription and translation (Thiele et al, 2009) will have an important function in elucidating the mechanisms that contribute the most to altered phenotypes (Lewis et al, 2009a). In addition, reconstruction of the transcriptional regulation network will aid in identifying the control of expression changes seen in the other systems.
Proteomic and transcriptomic data from wild‐type and laboratory‐evolved strains of Escherichia coli are consistent with predicted pathway usage from optimal growth rate solutions.
In laboratory‐evolved strains, there is an upregulation of the pathways in the computed optimal growth states, and downregulation of non‐functional pathways.
Known regulatory mechanisms are only partially responsible for altered metabolic pathway activity.
Abstract Each year infants, children and young adults die suddenly and unexpectedly. In many cases the cause of death can be elucidated by medico-legal autopsy, however, a significant number of these ...cases remain unexplained despite a detailed postmortem investigation and are labeled as sudden unexplained death (SUD). Post-mortem genetic testing, so called molecular autopsy, revealed that primary arrhythmogenic disorders including long QT syndrome and catecholaminergic polymorphic ventricular tachycardia (CPVT) may account for a certain number of these cases. Because of the inheritance of these diseases, close relatives of the deceased may also at potential risk of carrying fatal cardiac disorders. Therefore, advanced diagnostic analyses, genetic counseling and interdisciplinary collaboration should be integral parts of clinical and forensic practice. In the present study, we performed mutation analyses of the major genes causing cardiac channelopathies in 15 SUD cases. In four cases we found putative pathogenic mutations in cardiac ion channel genes. Clinical and genetic examination of family members of SUD victims was also performed and affected family members were identified. This study demonstrates that molecular genetic screening needs to become an inherent part of the postmortem examination. This will enhance the ability of screening family members of SUD victims who may be at risk. The present data also illustrate that detection and follow up of familial cases of sudden death is challenging and requires a close multidisciplinary collaboration between different medical disciplines, with great responsibility for the forensic pathologist.
Meningiomas are among the most frequent intracranial tumors. The secretory variant of meningioma is characterized by glandular differentiation, formation of intracellular lumina and pseudopsammoma ...bodies, expression of a distinct pattern of cytokeratins and clinically by pronounced perifocal brain edema. Here we describe whole-exome sequencing analysis of DNA from 16 secretory meningiomas and corresponding constitutional tissues. All secretory meningiomas invariably harbored a mutation in both
KLF4
and
TRAF7
. Validation in an independent cohort of 14 secretory meningiomas by Sanger sequencing or derived cleaved amplified polymorphic sequence (dCAPS) assay detected the same pattern, with
KLF4
mutations observed in a total of 30/30 and
TRAF7
mutations in 29/30 of these tumors. All
KLF4
mutations were identical, affected codon 409 and resulted in a lysine to glutamine exchange (K409Q).
KLF4
mutations were not found in 89 non-secretory meningiomas, 267 other intracranial tumors including gliomas, glioneuronal tumors, pituitary adenomas and metastases, 59 peripheral nerve sheath tumors and 52 pancreatic tumors.
TRAF7
mutations were restricted to the WD40 domains. While
KLF4
mutations were exclusively seen in secretory meningiomas,
TRAF7
mutations were also observed in 7/89 (8 %) of non-secretory meningiomas.
KLF4
and
TRAF7
mutations were mutually exclusive with
NF2
mutations. In conclusion, our findings suggest an essential contribution of combined
KLF4
K409Q and
TRAF7
mutations in the genesis of secretory meningioma and demonstrate a role for
TRAF7
alterations in other non-NF2 meningiomas.
Reactivation of the telomerase reverse transcriptase gene TERT is a central feature for unlimited proliferation of the majority of cancers. However, the underlying regulatory processes are only ...partly understood.
We assembled regulator binding information from serveral sources to construct a generic human and mouse gene regulatory network. Advancing our "Mixed Integer linear Programming based Regulatory Interaction Predictor" (MIPRIP) approach, we identified the most common and cancer-type specific regulators of TERT across 19 different human cancers. The results were validated by using the well-known TERT regulation by the ETS1 transcription factor in a subset of melanomas with mutations in the TERT promoter. Our improved MIPRIP2 R-package and the associated generic regulatory networks are freely available at https://github.com/KoenigLabNM/MIPRIP.
MIPRIP 2.0 identified common as well as tumor type specific regulators of TERT. The software can be easily applied to transcriptome datasets to predict gene regulation for any gene and disease/condition under investigation.
The immunosuppressive tumor microenvironment (TME) established by tumor cells, stromal cells and inhibitory immune cells counteracts the function of tumor reactive T cells. Tumor associated ...macrophages (TAMs) showing functional plasticity contribute to this process as so called M2-like macrophages can suppress the function of effector T cells and promote their differentiation into regulatory T cells (Tregs). Furthermore, tumor antigen specific CD4
T effector cells can essentially sustain anti-tumoral immune responses as shown for various tumor entities, thus suggesting that cognate interaction between tumor antigen-specific CD4
Th1 cells and TAMs might shift the intra-tumoral M1/M2 ratio toward M1. This study demonstrates repolarization of M2-like PECs upon MHC II-restricted interaction with tumor specific CD4
Th1 cells
as shown by extensive gene and protein expression analyses. Moreover, adoptive transfer of OVA-specific OT-II cells into C57BL/6 mice bearing OVA expressing IA
tumors resulted in increased accumulation of M1-like TAMs with enhanced M1 associated gene and protein expression profiles. Thus, this paper highlights a so far underestimated function of the CD4
Th1/TAM axis in re-conditioning the immunosuppressive tumor microenvironment.
This paper addresses the assessment of the effects of automation components on the design of service networks in rail freight. We define three automation scenarios comprising different automation ...degrees and discuss changes regarding the processes in the nodes of consolidation-based rail freight networks, so-called classification yards. We incorporate these process changes into a scheduled service network design model in order to evaluate structural differences regarding the design of these networks. The model is applied to a real-world case study in which we quantify effects of automation components in a part of the German single wagonload network. The results indicate a considerable potential in terms of cost savings as well as travel time reductions. A sensitivity analysis shows the effects of a possible decrease of shunting costs.
•Automation components streamline processes in classification yards.•Service networks need to be adapted to exploit process transformations in yards.•Tailored service networks yield lower transport times and cost reductions.
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