RNA Interference Mediated Suppression of Tn-Caspase-1 as a means of investigating apoptosis and improving recombinant protein production in Trichoplusia ni cells

The baculovirus expression system has proven to be a robust and versatile system for recombinant protein production in insect cells. A wide range of promoters is available for the facile expression of transgenes, and yields of up to 50% of total protein have been reported. However, in many cases production is decreased as a result of proteases and host cell apoptosis. To combat this problem, RNA interference (RNAi) has been used as a metabolic engineering tool to knockdown host genes responsible for decreasing the yield of recombinant protein. A novel caspase (Tn caspase-1) derived from Trichoplusia ni cells has been identified and characterized. Through modulation of caspase levels via either RNAi or through interaction with baculovirus protein p35, the overall level of apoptosis present in cell culture has been decreased. In addition, the use of in vitro RNAi targeted against Tn caspase-1 has increased the production of recombinant green fluorescent protein. To further study the effect of suppressing Tn caspase-1, a stable cell line producing in vivo RNAi was developed, resulting in a nearly 90% decrease in caspase enzymatic activity. This suppression was able to improve culture viability under adverse conditions and increase recombinant protein production levels up to two-fold that of standard cells

Accurate and precise viral quantification for rapid vaccine development in- process production monitoring using Radiance® Laser Force Cytology\u3csup\u3eTM

The biopharmaceutical world is evolving rapidly, bringing with it the need for technologies to support this fast-paced and changing environment. Trends in biomanufacturing are moving towards shortened development cycles as companies balance increased productivity requirements with the goal of reducing costs while at the same time ensuring production consistencies are met and batch out of specification (OOS) and failure events are minimized. LumaCyte’s Radiance® instrument using Laser Force Cytology™ (LFC), a combination of advanced optics and microfluidics to rapidly analyze single cells based upon their intrinsic biochemical and biophysical cellular properties and without the need for antibodies or labels. Subtle cellular changes can be precisely captured with Radiance’s automated workflow enabling new capabilities for measuring real-time product quality attributes to support R&D, process development and manufacturing needs across the biopharmaceutical industry. In this poster, LumaCyte demonstrates how tedious infectivity assays such as plaque and TCID50 can be replaced by Radiance’s rapid viral infectivity quantification assay to provide significant shorter time to result (TTR), reduced labor, and improved data quality and consistency. In addition, the bioproduction of vaccines, viral vectors or VLPs can be monitored in real-time, enabling rapid optimization of key processes and increasing process knowledge. As a result, product yield can be increased using the same inputs and the likelihood of OOS events can be reduced. Radiance applications in oncolytic virus research and neutralization assays are presented as well. Overall, LFC delivers faster TTR and improved data quality for vaccine analytics from R&D to manufacturing

Laser force cytology for rapid quantification of viral infectivity

The quantification of viral infectivity is an integral step at multiple stages in the process of virally producing recombinant protein, studying the mechanism of viral infection, and developing vaccines. Accurate measurements of infectivity allow for consistent infection and expansion, maximum yield, and assurance that time or environmental conditions have not degraded product quality. Traditional methods to assess infectivity, including the end-point dilution assay (TCID50) and viral plaque assay, are slow, labor intensive, and can vary depending upon the skill and experience of the user. Application of Laser Force Cytology (LFC) for the rapid detection and quantification of viral infection will be presented and discussed for several viral systems in the context of improving the development and production of vaccines. LumaCyte’s Radiance™ instrument is an automated cell analyzer and sorter that measures the optical force, size, shape, and deformability and captures images of single cells. By measuring the intrinsic properties of single cells, cellular changes due to viral infection can be rapidly and objectively quantitated. LFC is very sensitive to agents that perturb cellular structures or change biochemical composition. High quality viral infectivity measurements can be made in a fraction of the time, labor, and cost of traditional assays such as plaque or endpoint dilution. For in-process automated bioreactor monitoring, infectivity can be measured by Radiance in near real-time throughout the process, allowing critical feedback control and optimization. The measurement speed and data quality of LFC / Radiance serve to enhance vaccine development, process optimization/scale-up, and manufacturing to ultimately improve the delivery of vaccines to patients

The SV40 Late Protein VP4 Is a Viroporin that Forms Pores to Disrupt Membranes for Viral Release

Nonenveloped viruses are generally released by the timely lysis of the host cell by a poorly understood process. For the nonenveloped virus SV40, virions assemble in the nucleus and then must be released from the host cell without being encapsulated by cellular membranes. This process appears to involve the well-controlled insertion of viral proteins into host cellular membranes rendering them permeable to large molecules. VP4 is a newly identified SV40 gene product that is expressed at late times during the viral life cycle that corresponds to the time of cell lysis. To investigate the role of this late expressed protein in viral release, water-soluble VP4 was expressed and purified as a GST fusion protein from bacteria. Purified VP4 was found to efficiently bind biological membranes and support their disruption. VP4 perforated membranes by directly interacting with the membrane bilayer as demonstrated by flotation assays and the release of fluorescent markers encapsulated into large unilamellar vesicles or liposomes. The central hydrophobic domain of VP4 was essential for membrane binding and disruption. VP4 displayed a preference for membranes comprised of lipids that replicated the composition of the plasma membranes over that of nuclear membranes. Phosphatidylethanolamine, a lipid found at high levels in bacterial membranes, was inhibitory against the membrane perforation activity of VP4. The disruption of membranes by VP4 involved the formation of pores of ∼3 nm inner diameter in mammalian cells including permissive SV40 host cells. Altogether, these results support a central role of VP4 acting as a viroporin in the perforation of cellular membranes to trigger SV40 viral release

A Type 1 Diabetes Genetic Risk Score Can Identify Patients With GAD65 Autoantibody-Positive Type 2 Diabetes Who Rapidly Progress to Insulin Therapy

This is the author accepted manuscript. The final version is available from American Diabetes Association via the DOI in this record.Objective Progression to insulin therapy in clinically diagnosed type 2 diabetes is highly variable. GAD65 autoantibodies (GADA) are associated with faster progression, but their predictive value is limited. We aimed to determine if a Type 1 Diabetes Genetic Risk Score (T1DGRS) could predict rapid progression to insulin treatment over and above GADA testing. Research Design and Methods We examined the relationship between T1DGRS, GADA (negative or positive) and rapid insulin requirement (within 5 years) using Kaplan-Meier survival analysis and Cox regression in 8,608 participants with clinical type 2 diabetes (onset >35 years, treated without insulin for ≥6 months). T1DGRS was analyzed both continuously (as standardized scores) and categorized based on previously reported centiles of a type 1 diabetes population (50th (high)). Results In GADA positive participants (3.3%), those with higher T1DGRS progressed to insulin more quickly: Probability of insulin requirement at five years [95% CI]: 47.9%[35.0%,62.78%] (high T1DGRS) vs 27.6%[20.5%,36.5%] (medium T1DGRS) vs 17.6%[11.2%,27.2%] (low T1DGRS), p=0.001. In contrast T1DGRS did not predict rapid insulin requirement in GADA negative participants (p=0.4). In Cox regression analysis with adjustment for age of diagnosis, BMI and cohort, T1DGRS was independently associated with time to insulin only in the presence of GADA: hazard ratio per SD increase 1.48 (1.15,1.90), p=0.002. Conclusions A Type 1 Diabetes Genetic Risk Score alters the clinical implications of a positive GADA test in patients with clinical type 2 diabetes, and is independent of and additive to clinical features.The Wellcome Trust United Kingdom Type 2 Diabetes Case Control Collection (GoDARTS) was funded by The Wellcome Trust (084727/Z/08/Z, 085475/Z/08/Z, 085475/B/08/Z) and as part of the EU IMI-SUMMIT program. GADA assessment in GoDARTS and DCS was funded by EU Innovative Medicines Initiative 115317 (DIRECT), resources of which are composed of financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013), and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies in kind contribution. The DCS cohort was partially funded by the Netherlands Organization for Health Research and Development (Priority Medicines Elderly Programme 113102006). The Diabetes Alliance for Research in England (DARE) study was funded by the Wellcome Trust and supported by the Exeter NIHR Clinical Research Facility. The MASTERMIND study was funded by the UK Medical Research Council (MR/N00633X/) and supported by the NIHR Exeter Clinical Research Facility. The PRIBA study was funded by the National Institute for Health Research (U.K.) (DRF-2010-03-72) and supported by the NIHR Exeter Clinical Research Facility. B.M.S and A.T.H. are supported by the NIHR Exeter Clinical Research Facility. T.J.M. is a National Institute for Health Research Senior Clinical Senior Lecturer. E.R.P. is a Wellcome Trust New Investigator (102820/Z/13/Z). A.T.H. is a Wellcome Trust Senior Investigator and NIHR Senior Investigator. R.A.O is supported by a Diabetes UK Harry Keen Fellowship (16/0005529). A.G.J. is supported by an NIHR Clinician Scientist award (CS-2015-15-018)

Global Diversity of Sponges (Porifera)

With the completion of a single unified classification, the Systema Porifera (SP) and subsequent development of an online species database, the World Porifera Database (WPD), we are now equipped to provide a first comprehensive picture of the global biodiversity of the Porifera. An introductory overview of the four classes of the Porifera is followed by a description of the structure of our main source of data for this paper, the WPD. From this we extracted numbers of all ‘known’ sponges to date: the number of valid Recent sponges is established at 8,553, with the vast majority, 83%, belonging to the class Demospongiae. We also mapped for the first time the species richness of a comprehensive set of marine ecoregions of the world, data also extracted from the WPD. Perhaps not surprisingly, these distributions appear to show a strong bias towards collection and taxonomy efforts. Only when species richness is accumulated into large marine realms does a pattern emerge that is also recognized in many other marine animal groups: high numbers in tropical regions, lesser numbers in the colder parts of the world oceans. Preliminary similarity analysis of a matrix of species and marine ecoregions extracted from the WPD failed to yield a consistent hierarchical pattern of ecoregions into marine provinces. Global sponge diversity information is mostly generated in regional projects and resources: results obtained demonstrate that regional approaches to analytical biogeography are at present more likely to achieve insights into the biogeographic history of sponges than a global perspective, which appears currently too ambitious. We also review information on invasive sponges that might well have some influence on distribution patterns of the future

Targeting Huntington’s disease through histone deacetylases

Huntington’s disease (HD) is a debilitating neurodegenerative condition with significant burdens on both patient and healthcare costs. Despite extensive research, treatment options for patients with this condition remain limited. Aberrant post-translational modification (PTM) of proteins is emerging as an important element in the pathogenesis of HD. These PTMs include acetylation, phosphorylation, methylation, sumoylation and ubiquitination. Several families of proteins are involved with the regulation of these PTMs. In this review, I discuss the current evidence linking aberrant PTMs and/or aberrant regulation of the cellular machinery regulating these PTMs to HD pathogenesis. Finally, I discuss the evidence suggesting that pharmacologically targeting one of these protein families the histone deacetylases may be of potential therapeutic benefit in the treatment of HD

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation