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Register a new userDiversity in immunogenomics: the value and the challenge
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With the advent of high-throughput sequencing technologies, the fields of immunogenomics and adaptive immune receptor repertoire research are facing both opportunities and challenges. Adaptive immune receptor repertoire sequencing (AIRR-seq) has become an increasingly important tool to characterize T and B cell responses in settings of interest. However, the majority of AIRR-seq studies conducted so far were performed in individuals of European ancestry, restricting the ability to identify variation in human adaptive immune responses across populations and limiting their applications. As AIRR-seq studies depend on the ability to assign VDJ sequence reads to the correct germline gene segments, efforts to characterize the genomic loci that encode adaptive immune receptor genes in different populations are urgently needed. The availability of comprehensive germline gene databases and further applications of AIRR-seq studies to individuals of non-European ancestry will substantially enhance our understanding of human adaptive immune responses, promote the development of effective diagnostics and treatments, and eventually advance precision medicine.
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The Cambrian explosion is a grand challenge to science today and involves multidisciplinary study. This event is generally believed as a result of genetic innovations, environmental factors and ecological interactions, even though there are many conflicts on nature and timing of metazoan origins. The crux of the matter is that an entire roadmap of the evolution is missing to discern the biological complexity transition and to evaluate the critical role of the Cambrian explosion in the overall evolutionary context. Here we calculate the time of the Cambrian explosion by an innovative and accurate C-value clock; our result (560 million years ago) quite fits the fossil records. We clarify that the intrinsic reason of genome evolution determined the Cambrian explosion. A general formula for evaluating genome size of different species has been found, by which major questions of the C-value enigma can be solved and the genome size evolution can be illustrated. The Cambrian explosion is essentially a major transition of biological complexity, which corresponds to a turning point in genome size evolution. The observed maximum prokaryotic complexity is just a relic of the Cambrian explosion and it is supervised by the maximum information storage capability in the observed universe. Our results open a new prospect of studying metazoan origins and molecular evolution.
Increased availability of data and accessibility of computational tools in recent years have created unprecedented opportunities for scientific research driven by statistical analysis. Inherent limitations of statistics impose constrains on reliability of conclusions drawn from data but misuse of statistical methods is a growing concern. Significance, hypothesis testing and the accompanying P-values are being scrutinized as representing most widely applied and abused practices. One line of critique is that P-values are inherently unfit to fulfill their ostensible role as measures of scientific hypothesiss credibility. It has also been suggested that while P-values may have their role as summary measures of effect, researchers underappreciate the degree of randomness in the P-value. High variability of P-values would suggest that having obtained a small P-value in one study, one is, nevertheless, likely to obtain a much larger P-value in a similarly powered replication study. Thus, replicability of P-value is itself questionable. To characterize P-value variability one can use prediction intervals whose endpoints reflect the likely spread of P-values that could have been obtained by a replication study. Unfortunately, the intervals currently in use, the P-intervals, are based on unrealistic implicit assumptions. Namely, P-intervals are constructed with the assumptions that imply substantial chances of encountering large values of effect size in an observational study, which leads to bias. As an alternative to P-intervals, we develop a method that gives researchers flexibility by providing them with the means to control these assumptions. Unlike endpoints of P-intervals, endpoints of our intervals are directly interpreted as probabilistic bounds for replication P-values and are resistant to selection bias contingent upon approximate prior knowledge of the effect size distribution.
We derive an invertible transform linking two widely used measures of species diversity: phylogenetic diversity and the expected proportions of segregating (non-constant) sites. We assume a bi-allelic, symmetric, finite site model of substitution. Like the Hadamard transform of Hendy and Penny, the transform can be expressed completely independent of the underlying phylogeny. Our results bridge work on diversity from two quite distinct scientific communities.
Microorganisms, such as bacteria, are one of the first targets of nanoparticles in the environment. In this study, we tested the effect of two nanoparticles, ZnO and TiO2, with the salt ZnSO4 as the control, on the Gram-positive bacterium Bacillus subtilis by 2D gel electrophoresis-based proteomics. Despite a significant effect on viability (LD50), TiO2 NPs had no detectable effect on the proteomic pattern, while ZnO NPs and ZnSO4 significantly modified B. subtilis metabolism. These results allowed us to conclude that the effects of ZnO observed in this work were mainly attributable to Zn dissolution in the culture media. Proteomic analysis highlighted twelve modulated proteins related to central metabolism: MetE and MccB (cysteine metabolism), OdhA, AspB, IolD, AnsB, PdhB and YtsJ (Krebs cycle) and XylA, YqjI, Drm and Tal (pentose phosphate pathway). Biochemical assays, such as free sulfhydryl, CoA-SH and malate dehydrogenase assays corroborated the observed central metabolism reorientation and showed that Zn stress induced oxidative stress, probably as a consequence of thiol chelation stress by Zn ions. The other patterns affected by ZnO and ZnSO4 were the stringent response and the general stress response. Nine proteins involved in or controlled by the stringent response showed a modified expression profile in the presence of ZnO NPs or ZnSO4: YwaC, SigH, YtxH, YtzB, TufA, RplJ, RpsB, PdhB and Mbl. An increase in the ppGpp concentration confirmed the involvement of the stringent response during a Zn stress. All these metabolic reorientations in response to Zn stress were probably the result of complex regulatory mechanisms including at least the stringent response via YwaC.
For environmental problems such as global warming future costs must be balanced against present costs. This is traditionally done using an exponential function with a constant discount rate, which reduces the present value of future costs. The result is highly sensitive to the choice of discount rate and has generated a major controversy as to the urgency for immediate action. We study analytically several standard interest rate models from finance and compare their properties to empirical data. From historical time series for nominal interest rates and inflation covering 14 countries over hundreds of years, we find that extended periods of negative real interest rates are common, occurring in many epochs in all countries. This leads us to choose the Ornstein-Uhlenbeck model, in which real short run interest rates fluctuate stochastically and can become negative, even if they revert to a positive mean value. We solve the model in closed form and prove that the long-run discount rate is always less than the mean; indeed it can be zero or even negative, despite the fact that the mean short term interest rate is positive. We fit the parameters of the model to the data, and find that nine of the countries have positive long run discount rates while five have negative long-run discount rates. Even if one rejects the countries where hyperinflation has occurred, our results support the low discounting rate used in the Stern report over higher rates advocated by others.
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