Model-assisted metabolic engineering of Escherichia coli for long chain alkane and alcohol production

Biologically-derived hydrocarbons are considered to have great potential as next-generation biofuels owing to the similarity of their chemical properties to contemporary diesel and jet fuels. However, the low yield of these hydrocarbons in biotechnological production is a major obstacle for commercialization. Several genetic and process engineering approaches have been adopted to increase the yield of hydrocarbon, but a model driven approach has not been implemented so far. Here, we applied a constraint-based metabolic modeling approach in which a variable demand for alkane biosynthesis was imposed, and co-varying reactions were considered as potential targets for further engineering of an E. coli strain already expressing cyanobacterial enzymes towards higher chain alkane production. The reactions that co-varied with the imposed alkane production were found to be mainly associated with the pentose phosphate pathway (PPP) and the lower half of glycolysis. An optimal modeling solution was achieved by imposing increased flux through the reaction catalyzed by glucose-6-phosphate dehydrogenase (zwf) and iteratively removing 7 reactions from the network, leading to an alkane yield of 94.2% of the theoretical maximum conversion determined by in silico analysis at a given biomass rate. To validate the in silico findings, we first performed pathway optimization of the cyanobacterial enzymes in E. coli via different dosages of genes, promoting substrate channelling through protein fusion and inducing substantial equivalent protein expression, which led to a 36-fold increase in alka(e)ne production from 2.8 mg/L to 102 mg/L. Further, engineering of E. coli based on in silico findings, including biomass constraint, led to an increase in the alka(e)ne titer to 425 mg/L (major components being 249 mg/L pentadecane and 160 mg/L heptadecene), a 148.6-fold improvement over the initial strain, respectively; with a yield of 34.2% of the theoretical maximum. The impact of model-assisted engineering was also tested for the production of long chain fatty alcohol, another commercially important molecule sharing the same pathway while differing only at the terminal reaction, and a titer of 1506 mg/L was achieved with a yield of 86.4% of the theoretical maximum. Moreover, the model assisted engineered strains had produced 2.54 g/L and 12.5 g/L of long chain alkane and fatty alcohol, respectively, in the bioreactor under fed-batch cultivation condition. Our study demonstrated successful implementation of a combined in silico modeling approach along with the pathway and process optimization in achieving the highest reported titers of long chain hydrocarbons in E. coli

Primary Neuroendocrine tumor of Uterus - A case report

Background: Endometrial neuroendocrine carcinoma is a rare histological subtype of endometrial cancer, divided into low-grade neuroendocrine carcinoma (carcinoid) and high grade neuroendocrine carcinoma (small cell & large cell neuroendocrine carcinoma).It is characterized by high invasiveness and poor prognosis. Small cell neuroendocrine carcinoma (SCNEC) is an extremely rare pathological type of endometrial carcinoma. Case Presentation: A 65 years old post-menopausal lady presented with vaginal bleeding. Baseline imaging showed soft tissue lesion of uterine origin with unusual findings and biopsy showed neuroendocrine features. Positron emission tomography (PET) scan showed metastatic disease and she underwent chemotherapy which had an interestingly excellent response. Conclusion: Despite being rare and aggressive, neuroendocrine tumors must be considered as a differential possibility on imaging. Specific tracer of PET imaging can be useful as a non-invasive tool to restage the disease and monitor treatment response as highlighted by the imaging aspects of this rare entity in our case

Large cell neuroendocrine carcinoma of cervix: A rare and distinct clinicopathological entity

Large cell neuroendocrine carcinoma (LCNEC) is a rare malignancy and accounts for only 1% of cervical malignancies. The mean age is 34 years, ranges from 21 to 62 years. It usually presents with abnormal Pap smear or vaginal bleeding. LCNEC shows an aggressive behavior, similar to lung counterpart, with early metastases to regional lymph nodes and liver, lung, bone and brain. Median survival is <2 years. Due to the rarity of this malignancy, the management of LCNEC is difficult and associated with uncertainty. An interdisciplinary approach is necessary because most studies investigating the treatment of neuroendocrine tumors have been performed in patients with tumors in organs other than cervix, mostly the lung and pancreas. The biology of LCNEC is different from the squamous cell carcinoma or adenocarcinoma of the cervix regarding a number of characteristics. LCNEC is more likely to invade the lymph nodes at the time of diagnosis and local and distant relapses occur more often in LCNEC. Overall survival is significantly poor compared to squamous cell carcinoma and adenocarcinoma of the cervi

Biophysical and structural studies reveal marginal stability of a crucial hydrocarbon biosynthetic enzyme acyl ACP reductase

Abstract Acyl-ACP reductase (AAR) is one of the two key cyanobacterial enzymes along with aldehyde deformylating oxygenase (ADO) involved in the synthesis of long-chain alkanes, a drop-in biofuel. The enzyme is prone to aggregation when expressed in Escherichia coli, leading to varying alkane levels. The present work attempts to investigate the crucial structural aspects of AAR protein associated with its stability and folding. Characterization by dynamic light scattering experiment and intact mass spectrometry revealed that recombinantly expressed AAR in E. coli existed in multiple-sized protein particles due to diverse lipidation. Interestingly, while thermal- and urea-based denaturation of AAR showed 2-state unfolding transition in circular dichroism and intrinsic fluorescent spectroscopy, the unfolding process of AAR was a 3-state pathway in GdnHCl solution suggesting that the protein milieu plays a significant role in dictating its folding. Apparent standard free energy $$\left( {\Delta {\text{G}}_{{{\text{NU}}}}^{{{\text{H}}_{2} {\text{O}}}} } \right)$$ Δ G NU H 2 O of ~ 4.5 kcal/mol for the steady-state unfolding of AAR indicated borderline stability of the protein. Based on these evidences, we propose that the marginal stability of AAR are plausible contributing reasons for aggregation propensity and hence the low catalytic activity of the enzyme when expressed in E. coli for biofuel production. Our results show a path for building superior biocatalyst for higher biofuel production