Modulating carbohydrate–protein interactions through glycoengineering of monoclonal antibodies to impact cancer physiology

Diverse glycans on proteins help impact cell and organism physiology along with drug activity. Since many protein-based biotherapeutics are glycosylated and these glycans have biological activity, there is a desire to engineer glycosylation for recombinant protein-based biotherapeutics. Engineered glycosylation can impact the recombinant protein efficacy and also influence many cell pathways by first changing glycan-protein interactions and consequently modulating disease physiologies. However, its complexity is enormous. Due to recent advances in glycoengineering, modulating protein-glycan interactions become more amenable to therapeutic approaches. Here, we discuss how engineered glycans contribute to therapeutic monoclonal antibodies (mAbs) in the treatment of cancers, how these glycoengineered therapeutic mAbs affect the transformed phenotypes and downstream cell pathways, and how systems biology can help in the next generation mAb glycoengineering process by aiding in data analysis and guiding engineering efforts to tailor mAb glycan and ultimately drug efficacy, safety and affordability

Sensing Characteristics of Side-Hole Fiber-Based Long-Period Grating

Long-period gratings (LPGs) have been fabricated in a side-hole fiber (SHF) by using a pulsed CO2 laser. Sensing characteristics of this SHF-LPG to temperature surrounding refractive index and bend have been investigated. Experimental results show that resonant wavelength of the SHF-LPG has a blue shift with temperature with sensitivity of −0.11 nm/°C, a blue shift with increasing sensitivity with surrounding refractive index ranging from 1.335 to 1.44 (the maximum sensitivity is achieved when the surrounding refractive index reaches the effective index of the fiber cladding), and a red shift with bend-direction-dependent sensitivity up to 9.36 nm/m−1

An Excitatory Circuit in the Perioculomotor Midbrain for Non-REM Sleep Control.

The perioculomotor (pIII) region of the midbrain was postulated as a sleep-regulating center in the 1890s but largely neglected in subsequent studies. Using activity-dependent labeling and gene expression profiling, we identified pIII neurons that promote non-rapid eye movement (NREM) sleep. Optrode recording showed that pIII glutamatergic neurons expressing calcitonin gene-related peptide alpha (CALCA) are NREM-sleep active; optogenetic and chemogenetic activation/inactivation showed that they strongly promote NREM sleep. Within the pIII region, CALCA neurons form reciprocal connections with another population of glutamatergic neurons that express the peptide cholecystokinin (CCK). Activation of CCK neurons also promoted NREM sleep. Both CALCA and CCK neurons project rostrally to the preoptic hypothalamus, whereas CALCA neurons also project caudally to the posterior ventromedial medulla. Activation of each projection increased NREM sleep. Together, these findings point to the pIII region as an excitatory sleep center where different subsets of glutamatergic neurons promote NREM sleep through both local reciprocal connections and long-range projections

Rapid neurogenesis through transcriptional activation in human stem cells

Advances in cellular reprogramming and stem cell differentiation now enable ex vivo studies of human neuronal differentiation. However, it remains challenging to elucidate the underlying regulatory programs because differentiation protocols are laborious and often result in low neuron yields. Here, we overexpressed two Neurogenin transcription factors in human-induced pluripotent stem cells and obtained neurons with bipolar morphology in 4 days, at greater than 90% purity. The high purity enabled mRNA and microRNA expression profiling during neurogenesis, thus revealing the genetic programs involved in the rapid transition from stem cell to neuron. The resulting cells exhibited transcriptional, morphological and functional signatures of differentiated neurons, with greatest transcriptional similarity to prenatal human brain samples. Our analysis revealed a network of key transcription factors and microRNAs that promoted loss of pluripotency and rapid neurogenesis via progenitor states. Perturbations of key transcription factors affected homogeneity and phenotypic properties of the resulting neurons, suggesting that a systems-level view of the molecular biology of differentiation may guide subsequent manipulation of human stem cells to rapidly obtain diverse neuronal types

Ribosome profiling-guided depletion of an mRNA increases cell growth rate and protein secretion

Recombinant protein production coopts the host cell machinery to provide high protein yields of industrial enzymes or biotherapeutics. However, since protein translation is energetically expensive and tightly controlled, it is unclear if highly expressed recombinant genes are translated as efficiently as host genes. Furthermore, it is unclear how the high expression impacts global translation. Here, we present the first genome-wide view of protein translation in an IgG-producing CHO cell line, measured with ribosome profiling. Through this we found that our recombinant mRNAs were translated as efficiently as the host cell transcriptome, and sequestered up to 15% of the total ribosome occupancy. During cell culture, changes in recombinant mRNA translation were consistent with changes in transcription, demonstrating that transcript levels influence specific productivity. Using this information, we identified the unnecessary resistance marker NeoR to be a highly transcribed and translated gene. Through siRNA knock-down of NeoR, we improved the production- and growth capacity of the host cell. Thus, ribosomal profiling provides valuable insights into translation in CHO cells and can guide efforts to enhance protein production

Recon 2.2: from reconstruction to model of human metabolism.

IntroductionThe human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.ObjectivesWe report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.MethodsRecon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.ResultsRecon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.ConclusionThrough these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001)