Dual thermal ecotypes coexist within a nearly genetically identical population of the unicellular marine cyanobacterium Synechococcus.

The extent and ecological significance of intraspecific functional diversity within marine microbial populations is still poorly understood, and it remains unclear if such strain-level microdiversity will affect fitness and persistence in a rapidly changing ocean environment. In this study, we cultured 11 sympatric strains of the ubiquitous marine picocyanobacterium Synechococcus isolated from a Narragansett Bay (RI) phytoplankton community thermal selection experiment. Thermal performance curves revealed selection at cool and warm temperatures had subdivided the initial population into thermotypes with pronounced differences in maximum growth temperatures. Curiously, the genomes of all 11 isolates were almost identical (average nucleotide identities of >99.99%, with >99% of the genome aligning) and no differences in gene content or single nucleotide variants were associated with either cool or warm temperature phenotypes. Despite a very high level of genomic similarity, sequenced epigenomes for two strains showed differences in methylation on genes associated with photosynthesis. These corresponded to measured differences in photophysiology, suggesting a potential pathway for future mechanistic research into thermal microdiversity. Our study demonstrates that present-day marine microbial populations can harbor cryptic but environmentally relevant thermotypes which may increase their resilience to future rising temperatures.

SARS-CoV-2 genomic diversity and the implications for qRT-PCR diagnostics and transmission.

The COVID-19 pandemic has sparked an urgent need to uncover the underlying biology of this devastating disease. Though RNA viruses mutate more rapidly than DNA viruses, there are a relatively small number of single nucleotide polymorphisms (SNPs) that differentiate the main SARS-CoV-2 lineages that have spread throughout the world. In this study, we investigated 129 RNA-seq data sets and 6928 consensus genomes to contrast the intra-host and inter-host diversity of SARS-CoV-2. Our analyses yielded three major observations. First, the mutational profile of SARS-CoV-2 highlights intra-host single nucleotide variant (iSNV) and SNP similarity, albeit with differences in C > U changes. Second, iSNV and SNP patterns in SARS-CoV-2 are more similar to MERS-CoV than SARS-CoV-1. Third, a significant fraction of insertions and deletions contribute to the genetic diversity of SARS-CoV-2. Altogether, our findings provide insight into SARS-CoV-2 genomic diversity, inform the design of detection tests, and highlight the potential of iSNVs for tracking the transmission of SARS-CoV-2.

Analyzing microbial evolution through gene and genome phylogenies.

Microbiome scientists critically need modern tools to explore and analyze microbial evolution. Often this involves studying the evolution of microbial genomes as a whole. However, different genes in a single genome can be subject to different evolutionary pressures, which can result in distinct gene-level evolutionary histories. To address this challenge, we propose to treat estimated gene-level phylogenies as data objects, and present an interactive method for the analysis of a collection of gene phylogenies. We use a local linear approximation of phylogenetic tree space to visualize estimated gene trees as points in low-dimensional Euclidean space, and address important practical limitations of existing related approaches, allowing an intuitive visualization of complex data objects. We demonstrate the utility of our proposed approach through microbial data analyses, including by identifying outlying gene histories in strains of Prevotella, and by contrasting Streptococcus phylogenies estimated using different gene sets. Our method is available as an open-source R package, and assists with estimating, visualizing, and interacting with a collection of bacterial gene phylogenies.

Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer's disease.

BACKGROUND: The Mnemonic Similarity Task (MST) is a popular memory task designed to assess hippocampal integrity. We assessed whether analyzing MST performance using a multinomial processing tree (MPT) cognitive model could detect individuals with elevated Alzheimer's disease (AD) biomarker status prior to cognitive decline. METHOD: We analyzed MST data from >200 individuals (young, cognitively healthy older adults and individuals with mild cognitive impairment [MCI]), a subset of which also had existing cerebrospinal fluid (CSF) amyloid beta (Aß) and phosphorylated tau (pTau) data using both traditional and model-derived approaches. We assessed how well each could predict age group, memory ability, MCI status, Aß, and pTau status using receiver operating characteristic analyses. RESULTS: Both approaches predicted age group membership equally, but MPT-derived metrics exceeded traditional metrics in all other comparisons. DISCUSSION: A MPT model of the MST can detect individuals with AD prior to cognitive decline, making it a potentially useful tool for screening and monitoring older adults during the asymptomatic phase of AD. HIGHLIGHTS: The MST, along with cognitive modeling, identifies individuals with memory deficits and cognitive impairment. Cognitive modeling of the MST identifies individuals with increased AD biomarkers prior to changes in cognitive function. The MST is a digital biomarker that identifies individuals at high risk of AD.

Parsing memory and nonmemory contributions to age-related declines in mnemonic discrimination performance: a hierarchical Bayesian diffusion decision modeling approach.

The mnemonic discrimination task (MDT) is a widely used cognitive assessment tool. Performance in this task is believed to indicate an age-related deficit in episodic memory stemming from a decreased ability to pattern-separate among similar experiences. However, cognitive processes other than memory ability might impact task performance. In this study, we investigated whether nonmnemonic decision-making processes contribute to the age-related deficit in the MDT. We applied a hierarchical Bayesian version of the Ratcliff diffusion model to the MDT performance of 26 younger and 31 cognitively normal older adults. It allowed us to decompose decision behavior in the MDT into different underlying cognitive processes, represented by specific model parameters. Model parameters were compared between groups, and differences were evaluated using the Bayes factor. Our results suggest that the age-related decline in MDT performance indicates a predominantly mnemonic deficit rather than differences in nonmnemonic decision-making processes. In addition, this mnemonic deficit might also involve a slowing in processes related to encoding and retrieval strategies, which are relevant for successful memory as well. These findings help to better understand what cognitive processes contribute to the age-related decline in MDT performance and may help to improve the diagnostic value of this popular task.

The wisdom of the crowd with partial rankings: A Bayesian approach implementing the Thurstone model in JAGS.

We develop a Bayesian method for aggregating partial ranking data using the Thurstone model. Our implementation is a JAGS graphical model that allows each individual to rank any subset of items, and provides an inference about the latent true ranking of the items and the relative expertise of each individual. We demonstrate the method by analyzing data from new experiments that collected partial ranking data. In one experiment, participants were assigned subsets of items to rank; in the other experiment, participants could choose how many and which items they ranked. We show that our method works effectively for both sorts of partial ranking in applications to US city populations and the chronology of US presidents. We discuss the potential of the method for studying the wisdom of the crowd and other research problems that require aggregating incomplete or partial rankings.

Conserved functions of prion candidates suggest a primeval role of protein self-templating.

Amyloid-based prions have simple structures, a wide phylogenetic distribution, and a plethora of functions in contemporary organisms, suggesting they may be an ancient phenomenon. However, this hypothesis has yet to be addressed with a systematic, computational, and experimental approach. Here we present a framework to help guide future experimental verification of candidate prions with conserved functions to understand their role in the early stages of evolution and potentially in the origins of life. We identified candidate prions in all high-quality proteomes available in UniProt computationally, assessed their phylogenomic distributions, and analyzed candidate-prion functional annotations. Of the 27 980 560 proteins scanned, 228 561 were identified as candidate prions (~0.82%). Among these candidates, there were 84 Gene Ontology (GO) terms conserved across the three domains of life. We found that candidate prions with a possible role in adaptation were particularly well-represented within this group. We discuss unifying features of candidate prions to elucidate the primeval roles of prions and their associated functions. Candidate prions annotated as transcription factors, DNA binding, and kinases are particularly well suited to generating diverse responses to changes in their environment and could allow for adaptation and population expansion into more diverse environments. We hypothesized that a relationship between these functions and candidate prions could be evolutionarily ancient, even if individual prion domains themselves are not evolutionarily conserved. Candidate prions annotated with these universally occurring functions potentially represent the oldest extant prions on Earth and are therefore excellent experimental targets.

Why Environmental Biomarkers Work: Transcriptome-Proteome Correlations and Modeling of Multistressor Experiments in the Marine Bacterium Trichodesmium.

Ocean microbial communities are important contributors to the global biogeochemical reactions that sustain life on Earth. The factors controlling these communities are being increasingly explored using metatranscriptomic and metaproteomic environmental biomarkers. Using published proteomes and transcriptomes from the abundant colony-forming cyanobacterium Trichodesmium (strain IMS101) grown under varying Fe and/or P limitation in low and high CO2, we observed robust correlations of stress-induced proteins and RNAs (i.e., involved in transport and homeostasis) that yield useful information on the nutrient status under low and/or high CO2. Conversely, transcriptional and translational correlations of many other central metabolism pathways exhibit broad discordance. A cellular RNA and protein production/degradation model demonstrates how biomolecules with small initial inventories, such as environmentally responsive proteins, achieve large increases in fold-change units as opposed to those with a higher basal expression and inventory such as metabolic systems. Microbial cells, due to their immersion in the environment, tend to show large adaptive responses in both RNA and protein that result in transcript-protein correlations. These observations and model results demonstrate multi-omic coherence for environmental biomarkers and provide the underlying mechanism for those observations, supporting the promise for global application in detecting responses to environmental stimuli in a changing ocean.

Long-Term m5C Methylome Dynamics Parallel Phenotypic Adaptation in the Cyanobacterium Trichodesmium.

A major challenge in modern biology is understanding how the effects of short-term biological responses influence long-term evolutionary adaptation, defined as a genetically determined increase in fitness to novel environments. This is particularly important in globally important microbes experiencing rapid global change, due to their influence on food webs, biogeochemical cycles, and climate. Epigenetic modifications like methylation have been demonstrated to influence short-term plastic responses, which ultimately impact long-term adaptive responses to environmental change. However, there remains a paucity of empirical research examining long-term methylation dynamics during environmental adaptation in nonmodel, ecologically important microbes. Here, we show the first empirical evidence in a marine prokaryote for long-term m5C methylome modifications correlated with phenotypic adaptation to CO2, using a 7-year evolution experiment (1,000+ generations) with the biogeochemically important marine cyanobacterium Trichodesmium. We identify m5C methylated sites that rapidly changed in response to high (750 µatm) CO2 exposure and were maintained for at least 4.5 years of CO2 selection. After 7 years of CO2 selection, however, m5C methylation levels that initially responded to high-CO2 returned to ancestral, ambient CO2 levels. Concurrently, high-CO2 adapted growth and N2 fixation rates remained significantly higher than those of ambient CO2 adapted cell lines irrespective of CO2 concentration, a trend consistent with genetic assimilation theory. These data demonstrate the maintenance of CO2-responsive m5C methylation for 4.5 years alongside phenotypic adaptation before returning to ancestral methylation levels. These observations in a globally distributed marine prokaryote provide critical evolutionary insights into biogeochemically important traits under global change.

The Hunt for Ancient Prions: Archaeal Prion-Like Domains Form Amyloid-Based Epigenetic Elements.

Prions, proteins that can convert between structurally and functionally distinct states and serve as non-Mendelian mechanisms of inheritance, were initially discovered and only known in eukaryotes, and consequently considered to likely be a relatively late evolutionary acquisition. However, the recent discovery of prions in bacteria and viruses has intimated a potentially more ancient evolutionary origin. Here, we provide evidence that prion-forming domains exist in the domain archaea, the last domain of life left unexplored with regard to prions. We searched for archaeal candidate prion-forming protein sequences computationally, described their taxonomic distribution and phylogeny, and analyzed their associated functional annotations. Using biophysical in vitro assays, cell-based and microscopic approaches, and dye-binding analyses, we tested select candidate prion-forming domains for prionogenic characteristics. Out of the 16 tested, eight formed amyloids, and six acted as protein-based elements of information transfer driving non-Mendelian patterns of inheritance. We also identified short peptides from our archaeal prion candidates that can form amyloid fibrils independently. Lastly, candidates that tested positively in our assays had significantly higher tyrosine and phenylalanine content than candidates that tested negatively, an observation that may help future archaeal prion predictions. Taken together, our discovery of functional prion-forming domains in archaea provides evidence that multiple archaeal proteins are capable of acting as prions-thus expanding our knowledge of this epigenetic phenomenon to the third and final domain of life and bolstering the possibility that they were present at the time of the last universal common ancestor.

A Bayesian method for measuring risk propensity in the Balloon Analogue Risk Task.

The Balloon Analogue Risk Task (BART) is widely-used to measure risk propensity in theoretical, clinical, and applied research. In the task, people choose either to pump a balloon to increase its value at the risk of the balloon bursting and losing all value, or to bank the current value of the balloon. Risk propensity is most commonly measured as the average number of pumps on trials for which the balloon does not burst. Burst trials are excluded because they necessarily underestimate the number of pumps people intended to make. However, their exclusion discards relevant information about people's risk propensity. A better measure of risk propensity uses the statistical method of censoring to incorporate all of the trials. We develop a new Bayesian method, based on censoring, for measuring both risk propensity and behavioral consistency in the BART. Through applications to previous data we demonstrate how the method can be extended to consider the correlation of risk propensity with external measures, and to compare differences in risk propensity between groups. We provide implementations of all of these methods in R, MATLAB, and the GUI-based statistical software JASP.

DMSP synthesis genes distinguish two types of DMSP producer phenotypes.

Dimethylsulfoniopropionate (DMSP) is an important organic carbon and sulfur source in the surface ocean that fuels microbial activity and significantly impacts Earth's climate. After three decades of research, the cellular role(s) of DMSP and environmental drivers of production remain enigmatic. Recent work suggests that cellular DMSP concentrations, and changes in these concentrations in response to environmental stressors, define two major groups of DMSP producers: high DMSP producers that contain ≥ 50 mM intracellular DMSP and low DMSP producers that contain < 50 mM. Here we show that two recently described DMSP synthesis genes (DSYB and TpMT2) may differentiate these two DMSP phenotypes. A survey of prokaryotic and eukaryotic isolates found a significant correlation between the presence of DSYB and TpMT2 genes and previous measurements of high and low DMSP concentrations, respectively. Phylogenetic analysis demonstrated that DSYB and TpMT2 form two distinct clades. DSYB and TpMT2 were also found to be globally abundant in in situ surface communities, and their taxonomic annotations were similar to those observed for isolates. The strong correlation of the DSYB and TpMT2 synthesis genes with high and low producer phenotypes establishes a foundation for direct quantification of DMSP producers, enabling significantly improved predictions of DMSP in situ.

Preclinical Evaluation of a Fluorescent Probe Targeting Receptor CDCP1 for Identification of Ovarian Cancer.

Optimal cytoreduction for ovarian cancer is often challenging because of aggressive tumor biology and advanced stage. It is a critical issue since the extent of residual disease after surgery is the key predictor of ovarian cancer patient survival. For a limited number of cancers, fluorescence-guided surgery has emerged as an effective aid for tumor delineation and effective cytoreduction. The intravenously administered fluorescent agent, most commonly indocyanine green (ICG), accumulates preferentially in tumors, which are visualized under a fluorescent light source to aid surgery. Insufficient tumor specificity has limited the broad application of these agents in surgical oncology including for ovarian cancer. In this study, we developed a novel tumor-selective fluorescent agent by chemically linking ICG to mouse monoclonal antibody 10D7 that specifically recognizes an ovarian cancer-enriched cell surface receptor, CUB-domain-containing protein 1 (CDCP1). 10D7ICG has high affinity for purified recombinant CDCP1 and CDCP1 that is located on the surface of ovarian cancer cells in vitro and in vivo. Our results show that intravenously administered 10D7ICG accumulates preferentially in ovarian cancer, permitting visualization of xenograft tumors in mice. The data suggest CDCP1 as a rational target for tumor-specific fluorescence-guided surgery for ovarian cancer.

Following the Astrobiology Roadmap: Origins, Habitability and Future Exploration.

Astrobiology asks three fundamental questions as outlined by the NASA Astrobiology Roadmap: 1. How did Life begin and evolve?; Is there Life elsewhere in the Universe?; and, What is the future of Life on Earth? As we gain perspective on how Life on Earth arose and adapted to its many niches, we too gain insight into how a planet achieves habitability. Here on Earth, microbial Life has evolved to exist in a wide range of habitats from aquatic systems to deserts, the human body, and the International Space Station (ISS). Landers, rovers, and orbiter missions support the search for signatures of Life beyond Earth, by generating data on surface and subsurface conditions of other worlds. These have provided evidence for water activity, supporting the potential for extinct or extant Life. To investigate the putative ecologies of these systems, we study extreme environments on Earth. Several locations on our planet provide analog settings to those we have detected or expect to find on neighboring and distant worlds. Whereas, the field of space biology uses the ISS and low gravity analogs to gain insight on how transplanted Earth-evolved organisms will respond to extraterrestrial environments. Modern genomics allows us to chronicle the genetic makeup of such organisms and provides an understanding of how Life adapts to various extreme environments.

Genomic mosaicism underlies the adaptation of marine Synechococcus ecotypes to distinct oceanic iron niches.

Phytoplankton are limited by iron (Fe) in ~40% of the world's oceans including high-nutrient low-chlorophyll (HNLC) regions. While low-Fe adaptation has been well-studied in large eukaryotic diatoms, less is known for small, prokaryotic marine picocyanobacteria. This study reveals key physiological and genomic differences underlying Fe adaptation in marine picocyanobacteria. HNLC ecotype CRD1 strains have greater physiological tolerance to low Fe congruent with their expanded repertoire of Fe transporter, storage and regulatory genes compared to other ecotypes. From metagenomic analysis, genes encoding ferritin, flavodoxin, Fe transporters and siderophore uptake genes were more abundant in low-Fe waters, mirroring paradigms of low-Fe adaptation in diatoms. Distinct Fe-related gene repertories of HNLC ecotypes CRD1 and CRD2 also highlight how coexisting ecotypes have evolved independent approaches to life in low-Fe habitats. Synechococcus and Prochlorococcus HNLC ecotypes likewise exhibit independent, genome-wide reductions of predicted Fe-requiring genes. HNLC ecotype CRD1 interestingly was most similar to coastal ecotype I in Fe physiology and Fe-related gene content, suggesting populations from these different biomes experience similar Fe-selective conditions. This work supports an improved perspective that phytoplankton are shaped by more nuanced Fe niches in the oceans than previously implied from mostly binary comparisons of low- versus high-Fe habitats and populations.

GToTree: a user-friendly workflow for phylogenomics.

SUMMARY: Genome-level evolutionary inference (i.e. phylogenomics) is becoming an increasingly essential step in many biologists' work. Accordingly, there are several tools available for the major steps in a phylogenomics workflow. But for the biologist whose main focus is not bioinformatics, much of the computational work required-such as accessing genomic data on large scales, integrating genomes from different file formats, performing required filtering, stitching different tools together etc.-can be prohibitive. Here I introduce GToTree, a command-line tool that can take any combination of fasta files, GenBank files and/or NCBI assembly accessions as input and outputs an alignment file, estimates of genome completeness and redundancy, and a phylogenomic tree based on a specified single-copy gene (SCG) set. Although GToTree can work with any custom hidden Markov Models (HMMs), also included are 13 newly generated SCG-set HMMs for different lineages and levels of resolution, built based on searches of ∼12 000 bacterial and archaeal high-quality genomes. GToTree aims to give more researchers the capability to make phylogenomic trees. AVAILABILITY AND IMPLEMENTATION: GToTree is open-source and freely available for download from: github.com/AstrobioMike/GToTree. It is implemented primarily in bash with helper scripts written in python. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Marine Synechococcus isolates representing globally abundant genomic lineages demonstrate a unique evolutionary path of genome reduction without a decrease in GC content.

Synechococcus, a genus of unicellular cyanobacteria found throughout the global surface ocean, is a large driver of Earth's carbon cycle. Developing a better understanding of its diversity and distributions is an ongoing effort in biological oceanography. Here, we introduce 12 new draft genomes of marine Synechococcus isolates spanning five clades and utilize ~100 environmental metagenomes largely sourced from the TARA Oceans project to assess the global distributions of the genomic lineages they and other reference genomes represent. We show that five newly provided clade-II isolates are by far the most representative of the recovered in situ populations (most 'abundant') and have biogeographic distributions distinct from previously available clade-II references. Additionally, these isolates form a subclade possessing the smallest genomes yet identified of the genus (2.14 ± 0.05Mbps; mean ± 1SD) while concurrently hosting some of the highest GC contents (60.67 ± 0.16%). This is in direct opposition to the pattern in Synechococcus's nearest relative, Prochlorococcus - wherein decreasing genome size has coincided with a strong decrease in GC content - suggesting this new subclade of Synechococcus appears to have convergently undergone genomic reduction relative to the rest of the genus, but along a fundamentally different evolutionary trajectory.

Dual Site-Specific Labeling of an Antibody Fragment through Sortase A and π-Clamp Conjugation.

The functionalization of proteins with different cargo molecules is highly desirable for a broad range of applications. However, the reproducible production of defined conjugates with multiple functionalities is a significant challenge. Herein, we report the dual site-specific labeling of an antibody fragment, utilizing the orthogonal Sortase A and π-clamp conjugation methods, and demonstrate that binding of the antibody fragment to its target receptor is retained after dual labeling.

Molecular and physiological evidence of genetic assimilation to high CO2 in the marine nitrogen fixer Trichodesmium.

Most investigations of biogeochemically important microbes have focused on plastic (short-term) phenotypic responses in the absence of genetic change, whereas few have investigated adaptive (long-term) responses. However, no studies to date have investigated the molecular progression underlying the transition from plasticity to adaptation under elevated CO2 for a marine nitrogen-fixer. To address this gap, we cultured the globally important cyanobacterium Trichodesmium at both low and high CO2 for 4.5 y, followed by reciprocal transplantation experiments to test for adaptation. Intriguingly, fitness actually increased in all high-CO2 adapted cell lines in the ancestral environment upon reciprocal transplantation. By leveraging coordinated phenotypic and transcriptomic profiles, we identified expression changes and pathway enrichments that rapidly responded to elevated CO2 and were maintained upon adaptation, providing strong evidence for genetic assimilation. These candidate genes and pathways included those involved in photosystems, transcriptional regulation, cell signaling, carbon/nitrogen storage, and energy metabolism. Conversely, significant changes in specific sigma factor expression were only observed upon adaptation. These data reveal genetic assimilation as a potentially adaptive response of Trichodesmium and importantly elucidate underlying metabolic pathways paralleling the fixation of the plastic phenotype upon adaptation, thereby contributing to the few available data demonstrating genetic assimilation in microbial photoautotrophs. These molecular insights are thus critical for identifying pathways under selection as drivers in plasticity and adaptation.

A simple and flexible Bayesian method for inferring step changes in cognition.

Human behavioral data often show patterns of sudden change over time. Sometimes the causes of these step changes are internal, such as learning curves changing abruptly when a learner implements a new rule. Sometimes the cause is external, such as people's opinions about a topic changing in response to a new relevant event. Detecting change points in sequences of binary data is a basic statistical problem with many existing solutions, but these solutions rarely seem to be used in psychological modeling. We develop a simple and flexible Bayesian approach to modeling step changes in cognition, implemented as a graphical model in JAGS. The model is able to infer how many change points are justified by the data, as well as the locations of the change points. The basic model is also easily extended to include latent-mixture and hierarchical structures, allowing it to be tailored to specific cognitive modeling problems. We demonstrate the adequacy of this basic model by applying it to the classic Lindisfarne scribes problem, and the flexibility of the modeling approach is demonstrated through two new applications. The first involves a latent-mixture model to determine whether individuals learn categories incrementally or in discrete stages. The second involves a hierarchical model of crowd-sourced predictions about the winner of the US National Football League's Most Valuable Player for the 2016-2017 season.