In vitro antigen ELISA for quality control of tetanus vaccines

Consistency of production is recognised as an important aspect of vaccine manufacture and suitably validated in vitro assays are required for quality control testing of these products. For the manufacture and batch release of tetanus vaccines, antigen content and integrity, and degree of adsorption of antigen to the adjuvant are critical parameters that should be monitored for consistency. Here we describe the development and use of an Enzyme Linked Immunosorbent Assay (ELISA) to quantify tetanus antigen in combined vaccine products and to measure the degree of adsorption of antigen to adjuvant. Whilst the antigen assay cannot be assumed to predict potency for different products, it can be used as part of a panel of in vitro methods to provide a more informative product profile and to monitor trends in production. The antigen assay is particularly valuable for providing quantitative information on every final lot when modifications of in vivo potency tests, such as single dilution assays, are used.     

Classical Analysis of Variance Methods and Nonparametric Counterparts

The present investigation is concerned with the use of classical and nonparametric techniques in the analysis of experiments. Occasionally biometricians are confronted with results arising from an experiment with data which do not necessarily satisfy the assumption of Normality, the basis of the classical analysis of variance methods. Sometimes the biometrician handles such a situation by transformation of the observations and applying the classical techniques. Although this attack may lead to a satisfactory analysis, the results may be questionable in a number of cases. In analyzing continuous data without the Normality assumption nonparametric methods provide realistic alternatives. In this paper a number of problems will be discussed with and without the Normality assumption resulting in the well-known classical analysis and its nonparametric counterparts. Most of the nonparametric procedures to be discussed are based on ranks.     

Gut microbiota in wild and captive Guizhou snub‐nosed monkeys,Rhinopithecus brelichi

Many colobine species—including the endangered Guizhou snub‐nosed monkey (Rhinopithecus brelichi) are difficult to maintain in captivity and frequently exhibit gastrointestinal (GI) problems. GI problems are commonly linked to alterations in the gut microbiota, which lead us to examine the gut microbial communities of wild and captive R. brelichi. We used high‐throughput sequencing of the 16S rRNA gene to compare the gut microbiota of wild (N = 7) and captive (N = 8) R. brelichi. Wild monkeys exhibited increased gut microbial diversity based on the Chao1 but not Shannon diversity metric and greater relative abundances of bacteria in the Lachnospiraceae and Ruminococcaceae families. Microbes in these families digest complex plant materials and produce butyrate, a short chain fatty acid critical to colonocyte health. Captive monkeys had greater relative abundances of Prevotella and Bacteroides species, which degrade simple sugars and carbohydrates, like those present in fruits and cornmeal, two staples of the captive R. brelichi diet. Captive monkeys also had a greater abundance of Akkermansia species, a microbe that can thrive in the face of host malnutrition. Taken together, these findings suggest that poor health in captive R. brelichi may be linked to diet and an altered gut microbiota.     

Benzene and the risk of non-Hodgkin lymphoma: A review and meta-analysis of the literature

Benzene, an accepted leukemogen, has been suggested to cause other hemato- and lymphopoietic cancers. Here we review the published literature for non-Hodgkin lymphoma (NHL) and occupational exposure to benzene. Six cohorts, sixteen case–control studies and two studies of other designs were identified through keyword searches of bibliographic databases. Twenty-two of twenty-four studies found no association between NHL and ever exposed to benzene compared to never; a random-effects meta-analysis gave a pooled risk estimate of 1.11 (95% confidence interval 0.94–1.30). Our finding of no effect agrees with one of two previous meta-analyses. The other meta-analysis examined if high benzene exposure increased NHL risk but a lack of consistent exposure categories within the same metric should have precluded pooling risks by exposure level. Instead, we reviewed whether dose–response relationships existed. The best available data came from six studies where exposure was estimated from historical measurements and on the whole, no trends in risks of NHL with rising cumulative, average, peak, or duration of benzene exposure were found. NHL is a heterogeneous group of malignancies and although less well-studied, benzene was not associated with any NHL subtype. In conclusion, benzene at either low or high doses does not increase the risk of NHL.     

Characterization of binding proteins that recognize oligoglucoside elicitors of phytoalexin synthesis in soybean

We are studying the cellular signaling pathway leading to pterocarpan phytoalexin biosynthesis in soybean that is induced by a branched hepta-β-glucoside originally isolated from the mycelial walls of the phytopathogenic oomycete Phytophthora sojae. Our research has focused on the specific recognition of the hepta-β-glucoside elicitor by binding proteins in soybean cells. Elicitor-binding proteins with properties expected of physiological receptors for the hepta-β-glucoside elicitor have been identified in soybean root membranes. These elicitor-binding proteins co-migrate with a plasma membrane marker (vanadate-sensitive H⁺-ATPase) on linear sucrose density gradients. Binding of a radio-iodinated derivative of the hepta-β-glucoside elicitor by membrane-localized elicitor-binding proteins is specific, reversible, saturable, and of high affinity (K_d? 1 nM). After solubilization with the nonionic detergent, n-dodecylsucrose, the elicitor-binding proteins retain their high affinity (K_d= 1.8 nM) for the radiolabeled elicitor and their binding specificity for elicitor-active oligoglucosides. A direct correlation is observed between the ability of oligoglucosides to displace labeled elicitor from the elicitor-binding proteins and the elicitor activity of the oligosaccharides. Thus, the elicitor-binding proteins recognize the same structural elements of the hepta-β-glucoside elicitor that are essential for its phytoalexin-inducing activity, suggesting that the binding proteins are physiological receptors for the elicitor. Current research is directed toward the purification of the hepta-β-glucoside elicitor-binding proteins by using ligand affinity chromatography. Purification and characterization of the hepta-β-glucoside binding proteins are among the first steps toward elucidating how the hepta-β-glucoside elicitor triggers the signal transduction pathway that ultimately leads to the synthesis of phytoalexins in soybean.     

Preprocessing choices affect RNA velocity results for droplet scRNA-seq data

Experimental single-cell approaches are becoming widely used for many purposes, including investigation of the dynamic behaviour of developing biological systems. Consequently, a large number of computational methods for extracting dynamic information from such data have been developed. One example is RNA velocity analysis, in which spliced and unspliced RNA abundances are jointly modeled in order to infer a ‘direction of change’ and thereby a future state for each cell in the gene expression space. Naturally, the accuracy and interpretability of the inferred RNA velocities depend crucially on the correctness of the estimated abundances. Here, we systematically compare five widely used quantification tools, in total yielding thirteen different quantification approaches, in terms of their estimates of spliced and unspliced RNA abundances in five experimental droplet scRNA-seq data sets. We show that there are substantial differences between the quantifications obtained from different tools, and identify typical genes for which such discrepancies are observed. We further show that these abundance differences propagate to the downstream analysis, and can have a large effect on estimated velocities as well as the biological interpretation. Our results highlight that abundance quantification is a crucial aspect of the RNA velocity analysis workflow, and that both the definition of the genomic features of interest and the quantification algorithm itself require careful consideration.