Bacterial diversification through geological time

Numerous studies have estimated plant and animal diversification dynamics; however, no comparable rigorous estimates exist for bacteria—the most ancient and widespread form of life on Earth. Here, we analyse phylogenies comprising up to 448,112 bacterial lineages to reconstruct global bacterial diversification dynamics. To handle such large phylogenies, we developed methods based on the statistical properties of infinitely large trees. We further analysed sequencing data from 60 environmental studies to determine the fraction of extant bacterial diversity missing from the phylogenies—a crucial parameter for estimating speciation and extinction rates. We estimate that there are about 1.4–1.9 million extant bacterial lineages when lineages are defined by 99% similarity in the 16S ribosomal RNA gene, and that bacterial diversity has been continuously increasing over the past 1 billion years (Gyr). Recent bacterial extinction rates are estimated at 0.03–0.05 per lineage per million years (lineage^(–1) Myr^(–1)), and are only slightly below estimated recent bacterial speciation rates. Most bacterial lineages ever to have inhabited this planet are estimated to be extinct. Our findings disprove the notion that bacteria are unlikely to go extinct, and provide a valuable perspective on the evolutionary history of a domain of life with a sparse and cryptic fossil record

The REFOLD database: a tool for the optimization of protein expression and refolding

A large proportion of proteins expressed in Escherichia coli form inclusion bodies and thus require renaturation to attain a functional conformation for analysis. In this process, identifying and optimizing the refolding conditions and methodology is often rate limiting. In order to address this problem, we have developed REFOLD, a web-accessible relational database containing the published methods employed in the refolding of recombinant proteins. Currently, REFOLD contains >300 entries, which are heavily annotated such that the database can be searched via multiple parameters. We anticipate that REFOLD will continue to grow and eventually become a powerful tool for the optimization of protein renaturation. REFOLD is freely available at

Bacterial diversification through geological time

Numerous studies have estimated plant and animal diversification dynamics; however, no comparable rigorous estimates exist for bacteria—the most ancient and widespread form of life on Earth. Here, we analyse phylogenies comprising up to 448,112 bacterial lineages to reconstruct global bacterial diversification dynamics. To handle such large phylogenies, we developed methods based on the statistical properties of infinitely large trees. We further analysed sequencing data from 60 environmental studies to determine the fraction of extant bacterial diversity missing from the phylogenies—a crucial parameter for estimating speciation and extinction rates. We estimate that there are about 1.4–1.9 million extant bacterial lineages when lineages are defined by 99% similarity in the 16S ribosomal RNA gene, and that bacterial diversity has been continuously increasing over the past 1 billion years (Gyr). Recent bacterial extinction rates are estimated at 0.03–0.05 per lineage per million years (lineage^(–1) Myr^(–1)), and are only slightly below estimated recent bacterial speciation rates. Most bacterial lineages ever to have inhabited this planet are estimated to be extinct. Our findings disprove the notion that bacteria are unlikely to go extinct, and provide a valuable perspective on the evolutionary history of a domain of life with a sparse and cryptic fossil record

Photochemically produced SO2 in the atmosphere of WASP-39b

Photochemistry is a fundamental process of planetary atmospheres that regulates the atmospheric composition and stability1. However, no unambiguous photochemical products have been detected in exoplanet atmospheres so far. Recent observations from the JWST Transiting Exoplanet Community Early Release Science Program2,3 found a spectral absorption feature at 4.05 μm arising from sulfur dioxide (SO2) in the atmosphere of WASP-39b. WASP-39b is a 1.27-Jupiter-radii, Saturn-mass (0.28 MJ) gas giant exoplanet orbiting a Sun-like star with an equilibrium temperature of around 1,100 K (ref. 4). The most plausible way of generating SO2 in such an atmosphere is through photochemical processes5,6. Here we show that the SO2 distribution computed by a suite of photochemical models robustly explains the 4.05-μm spectral feature identified by JWST transmission observations7 with NIRSpec PRISM (2.7σ)8 and G395H (4.5σ)9. SO2 is produced by successive oxidation of sulfur radicals freed when hydrogen sulfide (H2S) is destroyed. The sensitivity of the SO2 feature to the enrichment of the atmosphere by heavy elements (metallicity) suggests that it can be used as a tracer of atmospheric properties, with WASP-39b exhibiting an inferred metallicity of about 10× solar. We further point out that SO2 also shows observable features at ultraviolet and thermal infrared wavelengths not available from the existing observations

Photochemically-produced SO2_2 in the atmosphere of WASP-39b

Photochemistry is a fundamental process of planetary atmospheres that regulates the atmospheric composition and stability. However, no unambiguous photochemical products have been detected in exoplanet atmospheres to date. Recent observations from the JWST Transiting Exoplanet Early Release Science Program found a spectral absorption feature at 4.05 $\mu$m arising from SO$_2$ in the atmosphere of WASP-39b. WASP-39b is a 1.27-Jupiter-radii, Saturn-mass (0.28 M$_J$) gas giant exoplanet orbiting a Sun-like star with an equilibrium temperature of $\sim$1100 K. The most plausible way of generating SO$_2$ in such an atmosphere is through photochemical processes. Here we show that the SO$_2$ distribution computed by a suite of photochemical models robustly explains the 4.05 $\mu$m spectral feature identified by JWST transmission observations with NIRSpec PRISM (2.7$\sigma$) and G395H (4.5$\sigma$). SO$_2$ is produced by successive oxidation of sulphur radicals freed when hydrogen sulphide (H$_2$S) is destroyed. The sensitivity of the SO$_2$ feature to the enrichment of the atmosphere by heavy elements (metallicity) suggests that it can be used as a tracer of atmospheric properties, with WASP-39b exhibiting an inferred metallicity of $\sim$10$\times$ solar. We further point out that SO$_2$ also shows observable features at ultraviolet and thermal infrared wavelengths not available from the existing observations.Comment: 39 pages, 14 figures, accepted to be published in Natur

Abstracts from the NIHR INVOLVE Conference 2017

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Supplement 4. C++ source code for the numerical simulations of the OUSS model and the population models. This code was also used to construct the CDF grid for the FAP correction.

<h2>File List</h2><div> <p><a href="Supplement4/Makefile">Makefile</a> (MD5: 1571a268f7b017373cf02034235d025d)</p> <p><a href="Supplement4/Readme.rtf">Readme.rtf</a> (MD5: 558b9c308189cdadd7f4c18b746eb18f)</p> <p><a href="Supplement4/source/Auxiliary.cpp">Auxiliary.cpp</a> (MD5: 02eeb96d83fe95532f8ccdda6ced3b9e)</p> <p><a href="Supplement4/source/Config.h">Config.h</a> (MD5: )d511149a4f32261c77b7d8be553ce491</p> <p><a href="Supplement4/source/FitOU.cpp">FitOU.cpp</a> (MD5: )eacb4f61818e9a5717eadf12a5759e3d</p> <p><a href="Supplement4/source/FitOUSS.cpp">FitOUSS.cpp</a> (MD5: )87d50d0ff06f874b68cca35a4ae16bc9</p> <p><a href="Supplement4/source/GompertzGrowthModel.h">GompertzGrowthModel.h</a> (MD5: 245e19cf1649a4ac5423926558a657f2)</p> <p><a href="Supplement4/source/Grid2DInterpolator.cpp">Grid2DInterpolator.cpp</a> (MD5: 4bf646da95188cea56e26d1f81caf589)</p> <p><a href="Supplement4/source/Grid2DInterpolator.h">Grid2DInterpolator.h</a> (MD5: 2cc0f61b0825331b6ba524205e75dfba)</p> <p><a href="Supplement4/source/Grid3DInterpolator.cpp">Grid3DInterpolator.cpp</a> (MD5:48e47b221aae950337526dd17d61e46e )</p> <p><a href="Supplement4/source/Grid3DInterpolator.h">Grid3DInterpolator.h</a> (MD5: ce9d9f631a393b98fe5b2b3ef6d0e24b)</p> <p><a href="Supplement4/source/GridMultilinearInterpolator.cpp">GridMultilinearInterpolator.cpp</a> (MD5: )8d6e62da84f6f1655e737b75249321de</p> <p><a href="Supplement4/source/GridMultilinearInterpolator.h">GridMultilinearInterpolator.h</a> (MD5: c9a326cf6b4ef6accf63cd6cd7b5983d)</p> <p><a href="Supplement4/source/InternalDefs.h">InternalDefs.h</a> (MD5: )e562ebdcb0ce950769b16f7e65f3b1d1</p> <p><a href="Supplement4/source/LogisticGrowthModel.h">LogisticGrowthModel.h</a> (MD5: )dff1b2ca16acb293e01efc2e941f0045</p> <p><a href="Supplement4/source/LombScargleSpectrum.h">LombScargleSpectrum.h</a> (MD5: 9eca3f459dbd96bcd7087184cac5a0fd)</p> <p><a href="Supplement4/source/main.cpp">main.cpp</a> (MD5: 1d24789f3c9589f753e282272597db76)</p> <p><a href="Supplement4/source/OrnsteinUhlenbeckModel.h">OrnsteinUhlenbeckModel.h</a> (MD5: e6bb199e430a68766491e68d5f5b5503)</p> <p><a href="Supplement4/source/PeakSignificance.cpp">PeakSignificance.cpp</a> (MD5: 00904b009f3ea273ce558ac6b9ebde04)</p> <p><a href="Supplement4/source/Points.h">Points.h</a> (MD5: )3ce2099bbbdcc4840aa4bf45d1f1b873</p> <p><a href="Supplement4/source/StochasticRungeKutta.h">StochasticRungeKutta.h</a> (MD5: )78e274a281beb35ed8617309a61de347</p> <p><a href="Supplement4/source/VectorArithmetics.h">VectorArithmetics.h</a> (MD5: f06222ddadecbb6ec5d0d3a7388b5f06)</p> <p><a href="Supplement4/source/STPlot/Makefile">STPlot/Makefile</a> (MD5: )</p> <p><a href="Supplement4/source/STPlot/sources/STColor.cpp">STColor.cpp</a> (MD5: f06222ddadecbb6ec5d0d3a7388b5f06)</p> <p><a href="Supplement4/source/STPlot/sources/STColor.h">STColor.h</a> (MD5: d2fbe6d7067fe383c9c67d3fb59289fa)</p> <p><a href="Supplement4/source/STPlot/sources/STPipe.cpp">STPipe.cpp</a> (MD5: da5e65bb44f7edcb6917e0b236bdcd5b)</p> <p><a href="Supplement4/source/STPlot/sources/STPipe.h">STPipe.h</a> (MD5: f0f692f8c8e08400ae9cace9fde76874)</p> <p><a href="Supplement4/source/STPlot/sources/STPlot.cpp">STPlot.cpp</a> (MD5: 2ce77e64526b46048926e2a293e5a79b)</p> <p><a href="Supplement4/source/STPlot/sources/STPlot.h">STPlot.h</a> (MD5: f783a60895433f031f9f5d388340126d)</p> <p><a href="Supplement4/source/STPlot/sources/STPlotDefaults.h">STPlotDefaults.h</a> (MD5: bc6bad182a2ad1ba233b288d4f72c8c7)</p> </div><h2>Description</h2><div> <p>C++ source code for the numerical simulations of the OUSS model and the population models. This code was also used to construct the CDF grid for the FAP correction. You will need a C++ compiler (e.g., GCC) to compile the code, as well as the ALGLIB library (free C++ version). The latter is needed for the L-BFGS optimization algorithm.</p> <p>The Makefile contains all necessary commands for compilation using GCC. Use "make" in your terminal/command line to execute it. More details are given in the Readme.rtf file as well as in the source files.</p> </div

Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem

The 16S ribosomal RNA gene is the most widely used marker gene in microbial ecology. Counts of 16S sequence variants, often in PCR amplicons, are used to estimate proportions of bacterial and archaeal taxa in microbial communities. Because different organisms contain different 16S gene copy numbers (GCNs), sequence variant counts are biased towards clades with greater GCNs. Several tools have recently been developed for predicting GCNs using phylogenetic methods and based on sequenced genomes, in order to correct for these biases. However, the accuracy of those predictions has not been independently assessed. Here, we systematically evaluate the predictability of 16S GCNs across bacterial and archaeal clades, based on ∼ 6,800 public sequenced genomes and using several phylogenetic methods. Further, we assess the accuracy of GCNs predicted by three recently published tools (PICRUSt, CopyRighter, and PAPRICA) over a wide range of taxa and for 635 microbial communities from varied environments. We find that regardless of the phylogenetic method tested, 16S GCNs could only be accurately predicted for a limited fraction of taxa, namely taxa with closely to moderately related representatives (≲15% divergence in the 16S rRNA gene). Consistent with this observation, we find that all considered tools exhibit low predictive accuracy when evaluated against completely sequenced genomes, in some cases explaining less than 10% of the variance. Substantial disagreement was also observed between tools (R2<0.5) for the majority of tested microbial communities. The nearest sequenced taxon index (NSTI) of microbial communities, i.e., the average distance to a sequenced genome, was a strong predictor for the agreement between GCN prediction tools on non-animal-associated samples, but only a moderate predictor for animal-associated samples. We recommend against correcting for 16S GCNs in microbiome surveys by default, unless OTUs are sufficiently closely related to sequenced genomes or unless a need for true OTU proportions warrants the additional noise introduced, so that community profiles remain interpretable and comparable between studies.Science, Faculty ofOther UBCBotany, Department ofMathematics, Department ofZoology, Department ofReviewedFacult

Appendix C. Bias and correction of the FAP estimator.

Bias and correction of the FAP estimator

Appendix F. Details of the statistical analysis of the GPDD.

Details of the statistical analysis of the GPDD