Metabolic engineering of yeast for increased production of cyclopropane fatty acids

Biological production of chemicals and fuels using whole cells is an important and growing segment of manufacturing and among the various forms, microorganisms are the most successfully utilized. In particular, yeasts such as Saccharomyces cerevisiae are both widely used production organisms and metabolic models for oleaginous yeasts. Fatty acid-containing lipids are one example of moderate value, highly versatile chemicals produced by yeasts that are used in a broad range of industries for lubrication, cosmetics, fuels and polymers. Production levels of standard fatty acids by yeasts has increased enormously over the past 10 years through the application of metabolic pathway engineering, flux analysis, computational approaches and to a lesser extent, bioprocessing improvements. Combined, these advances have brought yeast-based fatty acid production close to commercial reality. Functionalized fatty acids such as those containing hydroxyl or cyclopropyl groups are more valuable as chemical feedstocks and are an attractive target for yeast production as commercial supply is limited. Cyclopropane fatty acids, possessing a strained 3-membered ring and having a saturated chain, are especially attractive as they have application in cosmetics and specialty lubrication. However, cyclopropyl fatty acids present greater challenges for metabolic engineering as they are not produced naturally by yeast. Please click Additional Files below to see the full abstract

Lightweight Vision Transformer with Cross Feature Attention

Recent advances in vision transformers (ViTs) have achieved great performance in visual recognition tasks. Convolutional neural networks (CNNs) exploit spatial inductive bias to learn visual representations, but these networks are spatially local. ViTs can learn global representations with their self-attention mechanism, but they are usually heavy-weight and unsuitable for mobile devices. In this paper, we propose cross feature attention (XFA) to bring down computation cost for transformers, and combine efficient mobile CNNs to form a novel efficient light-weight CNN-ViT hybrid model, XFormer, which can serve as a general-purpose backbone to learn both global and local representation. Experimental results show that XFormer outperforms numerous CNN and ViT-based models across different tasks and datasets. On ImageNet1K dataset, XFormer achieves top-1 accuracy of 78.5% with 5.5 million parameters, which is 2.2% and 6.3% more accurate than EfficientNet-B0 (CNN-based) and DeiT (ViT-based) for similar number of parameters. Our model also performs well when transferring to object detection and semantic segmentation tasks. On MS COCO dataset, XFormer exceeds MobileNetV2 by 10.5 AP (22.7 -> 33.2 AP) in YOLOv3 framework with only 6.3M parameters and 3.8G FLOPs. On Cityscapes dataset, with only a simple all-MLP decoder, XFormer achieves mIoU of 78.5 and FPS of 15.3, surpassing state-of-the-art lightweight segmentation networks.Comment: Technical Repor

AIF Downregulation and Its Interaction with STK3 in Renal Cell Carcinoma

Apoptosis-inducing factor (AIF) plays a crucial role in caspase-independent programmed cell death by triggering chromatin condensation and DNA fragmentation. Therefore, it might be involved in cell homeostasis and tumor development. In this study, we report significant AIF downregulation in the majority of renal cell carcinomas (RCC). In a group of RCC specimens, 84% (43 out of 51) had AIF downregulation by immunohistochemistry stain. Additional 10 kidney tumors, including an oxyphilic adenoma, also had significant AIF downregulation by Northern blot analysis. The mechanisms of the AIF downregulation included both AIF deletion and its promoter methylation. Forced expression of AIF in RCC cell lines induced massive apoptosis. Further analysis revealed that AIF interacted with STK3, a known regulator of apoptosis, and enhanced its phosphorylation at Thr180. These results suggest that AIF downregulation is a common event in kidney tumor development. AIF loss may lead to decreased STK3 activity, defective apoptosis and malignant transformation

Self-organized Voids Revisited: Experimental Verification of the Formation Mechanism*

In this paper, several experiments were conducted to further clarify the formation mechanism of self organized void array induced by a single laser beam, including energy-related experiments, refractive-index-contrast-related experiments, depth-related experiments and effective-numerical-aperture experiment. These experiments indicate that the interface spherical aberration is indeed responsible for the formation of void arrays

A molecular toolkit of cross-feeding strains for engineering synthetic yeast communities

Engineered microbial consortia often have enhanced system performance and robustness compared with single-strain biomanufacturing production platforms. However, few tools are available for generating co-cultures of the model and key industrial host Saccharomyces cerevisiae. Here we engineer auxotrophic and overexpression yeast strains that can be used to create co-cultures through exchange of essential metabolites. Using these strains as modules, we engineered two- and three-member consortia using different cross-feeding architectures. Through a combination of ensemble modelling and experimentation, we explored how cellular (for example, metabolite production strength) and environmental (for example, initial population ratio, population density and extracellular supplementation) factors govern population dynamics in these systems. We tested the use of the toolkit in a division of labour biomanufacturing case study and show that it enables enhanced and tuneable antioxidant resveratrol production. We expect this toolkit to become a useful resource for a variety of applications in synthetic ecology and biomanufacturing

Serum glucose, lactate dehydrogenase and hypertension are mediators of the 1 effect of body mass index on severity of COVID-19

Background: COVID-19 has a broad clinical specturm. We investigated the role of serum markers measured on admission on severity as assessed at discharge and investigated those which relate to the effect of BMI on severity. Methods: Clinical and laboratory data form 610 COVID-19 cases hospitalised in the province of Zheijang, China was investigated as risk factors for severe COVID-19 (assessed by respiratory distress) compared to mild or common forms using logistic regression methods. Biochemical markers were correlated with severity using spearman correlations and a ROC analysis was used to determine the individual contribution of each of the biochemical markers on severity. We carried out formal mediation analyses to investigate the extent of the effect of body mass index (BMI) on COVID-19 severity mediated by hypertension, glycemia, Lactose Dehydrogenase (LDH) at the time of hospitalisation and C-Reactive Protein levels (CRP), in units of standard deviations. Results: The individual markers measured on admission contributing most strongly to prediction of COVID-19 severity as assessed at discharge were LDH, CRP and glucose. The proportion of the effect of BMI on severity of COVID-19 mediated by CRP, glycemia or hypertension we find that glucose mediated 79% (

The Effects of ATIR Blocker on the Severity of COVID-19 in Hypertensive Inpatients and Virulence of SARS-CoV-2 in Hypertensive hACE2 Transgenic Mice.

Angiotensin-converting enzyme 2 (ACE2) is required for the cellular entry of the severe acute respiratory syndrome coronavirus 2. ACE2, via the Ang-(1-7)-Mas-R axis, is part of the antihypertensive and cardioprotective effects of the renin-angiotensin system. We studied hospitalized COVID-19 patients with hypertension and hypertensive human(h) ACE2 transgenic mice to determine the outcome of COVID-19 with or without AT1 receptor (AT1R) blocker treatment. The severity of the illness and the levels of serum cardiac biomarkers (CK, CK-BM, cTnI), as well as the inflammation markers (IL-1, IL-6, CRP), were lesser in hypertensive COVID-19 patients treated with AT1R blockers than those treated with other antihypertensive drugs. Hypertensive hACE2 transgenic mice, pretreated with AT1R blocker, had increased ACE2 expression and SARS-CoV-2 in the kidney and heart, 1 day post-infection. We conclude that those hypertensive patients treated with AT1R blocker may be at higher risk for SARS-CoV-2 infection. However, AT1R blockers had no effect on the severity of the illness but instead may have protected COVID-19 patients from heart injury, via the ACE2-angiotensin1-7-Mas receptor axis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12265-021-10147-3