A total crapshoot? Evaluating bioinformatic decisions in animal diet metabarcoding analyses

Metabarcoding studies provide a powerful approach to estimate the diversity and abundance of organisms in mixed communities in nature. While strategies exist for optimizing sample and sequence library preparation, best practices for bioinformatic processing of amplicon sequence data are lacking in animal diet studies. Here we evaluate how decisions made in core bioinformatic processes, including sequence filtering, database design, and classification, can influence animal metabarcoding results. We show that denoising methods have lower error rates compared to traditional clustering methods, although these differences are largely mitigated by removing low‐abundance sequence variants. We also found that available reference datasets from GenBank and BOLD for the animal marker gene cytochrome oxidase I (COI) can be complementary, and we discuss methods to improve existing databases to include versioned releases. Taxonomic classification methods can dramatically affect results. For example, the commonly used Barcode of Life Database (BOLD) Classification API assigned fewer names to samples from order through species levels using both a mock community and bat guano samples compared to all other classifiers (vsearch‐SINTAX and q2‐feature‐classifier's BLAST + LCA, VSEARCH + LCA, and Naive Bayes classifiers). The lack of consensus on bioinformatics best practices limits comparisons among studies and may introduce biases. Our work suggests that biological mock communities offer a useful standard to evaluate the myriad computational decisions impacting animal metabarcoding accuracy. Further, these comparisons highlight the need for continual evaluations as new tools are adopted to ensure that the inferences drawn reflect meaningful biology instead of digital artifacts.     

RAPTURE (RAD capture) panel facilitates analyses characterizing sea lamprey reproductive ecology and movement dynamics

Genomic tools are lacking for invasive and native populations of sea lamprey (Petromyzon marinus). Our objective was to discover single nucleotide polymorphism (SNP) loci to conduct pedigree analyses to quantify reproductive contributions of adult sea lampreys and dispersion of sibling larval sea lampreys of different ages in Great Lakes tributaries. Additional applications of data were explored using additional geographically expansive samples. We used restriction site‐associated DNA sequencing (RAD‐Seq) to discover genetic variation in Duffins Creek (DC), Ontario, Canada, and the St. Clair River (SCR), Michigan, USA. We subsequently developed RAD capture baits to genotype 3,446 RAD loci that contained 11,970 SNPs. Based on RAD capture assays, estimates of variance in SNP allele frequency among five Great Lakes tributary populations (mean F_ST 0.008; range 0.00–0.018) were concordant with previous microsatellite‐based studies; however, outlier loci were identified that contributed substantially to spatial population genetic structure. At finer scales within streams, simulations indicated that accuracy in genetic pedigree reconstruction was high when 200 or 500 independent loci were used, even in situations of high spawner abundance (e.g., 1,000 adults). Based on empirical collections of larval sea lamprey genotypes, we found that age‐1 and age‐2 families of full and half‐siblings were widely but nonrandomly distributed within stream reaches sampled. Using the genomic scale set of SNP loci developed in this study, biologists can rapidly genotype sea lamprey in non‐native and native ranges to investigate questions pertaining to population structuring and reproductive ecology at previously unattainable scales.     

One size does not fit all: Customizing MCMC methods for hierarchical models using NIMBLE

Improved efficiency of Markov chain Monte Carlo facilitates all aspects of statistical analysis with Bayesian hierarchical models. Identifying strategies to improve MCMC performance is becoming increasingly crucial as the complexity of models, and the run times to fit them, increases. We evaluate different strategies for improving MCMC efficiency using the open‐source software NIMBLE (R package nimble) using common ecological models of species occurrence and abundance as examples. We ask how MCMC efficiency depends on model formulation, model size, data, and sampling strategy. For multiseason and/or multispecies occupancy models and for N‐mixture models, we compare the efficiency of sampling discrete latent states vs. integrating over them, including more vs. fewer hierarchical model components, and univariate vs. block‐sampling methods. We include the common MCMC tool JAGS in comparisons. For simple models, there is little practical difference between computational approaches. As model complexity increases, there are strong interactions between model formulation and sampling strategy on MCMC efficiency. There is no one‐size‐fits‐all best strategy, but rather problem‐specific best strategies related to model structure and type. In all but the simplest cases, NIMBLE's default or customized performance achieves much higher efficiency than JAGS. In the two most complex examples, NIMBLE was 10–12 times more efficient than JAGS. We find NIMBLE is a valuable tool for many ecologists utilizing Bayesian inference, particularly for complex models where JAGS is prohibitively slow. Our results highlight the need for more guidelines and customizable approaches to fit hierarchical models to ensure practitioners can make the most of occupancy and other hierarchical models. By implementing model‐generic MCMC procedures in open‐source software, including the NIMBLE extensions for integrating over latent states (implemented in the R package nimbleEcology), we have made progress toward this aim.     

Multivariate data analysis of growth medium trends affecting antibody glycosylation

Use of multivariate data analysis for the manufacturing of biologics has been increasing due to more widespread use of data-generating process analytical technologies (PAT) promoted by the US FDA. To generate a large dataset on which to apply these principles, we used an in-house model CHO DG44 cell line cultured in automated micro bioreactors alongside PAT with four commercial growth media focusing on antibody quality through N-glycosylation profiles. Using univariate analyses, we determined that different media resulted in diverse amounts of terminal galactosylation, high mannose glycoforms, and aglycosylation. Due to the amount of in-process data generated by PAT instrumentation, multivariate data analysis was necessary to ascertain which variables best modeled our glycan profile findings. Our principal component analysis revealed components that represent the development of glycoforms into terminally galacotosylated forms (G1F and G2F), and another that encompasses maturation out of high mannose glycoforms. The partial least squares model additionally incorporated metabolic values to link these processes to glycan outcomes, especially involving the consumption of glutamine. Overall, these approaches indicated a tradeoff between cellular productivity and product quality in terms of the glycosylation. This work illustrates the use of multivariate analytical approaches that can be applied to complex bioprocessing problems for identifying potential solutions.     

The fission yeast S-phase cyclin Cig2 can drive mitosis

Commitment to mitosis is regulated by cyclin-dependent kinase (CDK) activity. In the fission yeast Schizosaccharomyces pombe, the major B-type cyclin, Cdc13, is necessary and sufficient to drive mitotic entry. Furthermore, Cdc13 is also sufficient to drive S phase, demonstrating that a single cyclin can regulate alternating rounds of replication and mitosis, and providing the foundation of the quantitative model of CDK function. It has been assumed that Cig2, a B-type cyclin expressed only during S phase and incapable of driving mitosis in wild-type cells, was specialized for S-phase regulation. Here, we show that Cig2 is capable of driving mitosis. Cig2/CDK activity drives mitotic catastrophe—lethal mitosis in inviably small cells—in cells that lack CDK inhibition by tyrosine-phosphorylation. Moreover, Cig2/CDK can drive mitosis in the absence of Cdc13/CDK activity and constitutive expression of Cig2 can rescue loss of Cdc13 activity. These results demonstrate that in fission yeast, not only can the presumptive M-phase cyclin drive S phase, but the presumptive S-phase cyclin can drive M phase, further supporting the quantitative model of CDK function. Furthermore, these results provide an explanation, previously proposed on the basis of computational analyses, for the surprising observation that cells expressing a single-chain Cdc13-Cdc2 CDK do not require Y15 phosphorylation for viability. Their viability is due to the fact that in such cells, which lack Cig2/CDK complexes, Cdc13/CDK activity is unable to drive mitotic catastrophe.     

ArfGAP1 inhibits mTORC1 lysosomal localization and activation

The mammalian target of rapamycin complex 1 (mTORC1) integrates nutrients, growth factors, stress, and energy status to regulate cell growth and metabolism. Amino acids promote mTORC1 lysosomal localization and subsequent activation. However, the subcellular location or interacting proteins of mTORC1 under amino acid‐deficient conditions is not completely understood. Here, we identify ADP‐ribosylation factor GTPase‐activating protein 1 (ArfGAP1) as a crucial regulator of mTORC1. ArfGAP1 interacts with mTORC1 in the absence of amino acids and inhibits mTORC1 lysosomal localization and activation. Mechanistically, the membrane curvature‐sensing amphipathic lipid packing sensor (ALPS) motifs that bind to vesicle membranes are crucial for ArfGAP1 to interact with and regulate mTORC1 activity. Importantly, ArfGAP1 represses cell growth through mTORC1 and is an independent prognostic factor for the overall survival of pancreatic cancer patients. Our study identifies ArfGAP1 as a critical regulator of mTORC1 that functions by preventing the lysosomal transport and activation of mTORC1, with potential for cancer therapeutics.     

SARS-CoV-2 tropism,entry, replication,and propagation: Considerations for drug discovery and development

Since the initial report of the novel Coronavirus Disease 2019 (COVID-19) emanating from Wuhan, China, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread globally. While the effects of SARS-CoV-2 infection are not completely understood, there appears to be a wide spectrum of disease ranging from mild symptoms to severe respiratory distress, hospitalization, and mortality. There are no Food and Drug Administration (FDA)-approved treatments for COVID-19 aside from remdesivir; early efforts to identify efficacious therapeutics for COVID-19 have mainly focused on drug repurposing screens to identify compounds with antiviral activity against SARS-CoV-2 in cellular infection systems. These screens have yielded intriguing hits, but the use of nonhuman immortalized cell lines derived from non-pulmonary or gastrointestinal origins poses any number of questions in predicting the physiological and pathological relevance of these potential interventions. While our knowledge of this novel virus continues to evolve, our current understanding of the key molecular and cellular interactions involved in SARS-CoV-2 infection is discussed in order to provide a framework for developing the most appropriate in vitro toolbox to support current and future drug discovery efforts.