The effects of warming on the ecophysiology of two co-existing kelp species with contrasting distributions

The northeast Atlantic has warmed significantly since the early 1980s, leading to shifts in species distributions and changes in the structure and functioning of communities and ecosystems. This study investigated the effects of increased temperature on two co-existing habitat-forming kelps: Laminaria digitata, a northern boreal species, and Laminaria ochroleuca, a southern Lusitanian species, to shed light on mechanisms underpinning responses of trailing and leading edge populations to warming. Kelp sporophytes collected from southwest United Kingdom were maintained under 3 treatments: ambient temperature (12 °C), +3 °C (15 °C) and +6 °C (18 °C) for 16 days. At higher temperatures, L. digitata showed a decline in growth rates and Fv/Fm, an increase in chemical defence production and a decrease in palatability. In contrast, L. ochroleuca demonstrated superior growth and photosynthesis at temperatures higher than current ambient levels, and was more heavily grazed. Whilst the observed decreased palatability of L. digitata held at higher temperatures could reduce top-down pressure on marginal populations, field observations of grazer densities suggest that this may be unimportant within the study system. Overall, our study suggests that shifts in trailing edge populations will be primarily driven by ecophysiological responses to high temperatures experienced during current and predicted thermal maxima, and although compensatory mechanisms may reduce top-down pressure on marginal populations, this is unlikely to be important within the current biogeographical context. Better understanding of the mechanisms underpinning climate-driven range shifts is important for habitat-forming species like kelps, which provide organic matter, create biogenic structure and alter environmental conditions for associated communities

Community Mobility and Depressive Symptoms During the COVID-19 Pandemic in the United States

Importance Marked elevation in levels of depressive symptoms compared with historical norms have been described during the COVID-19 pandemic, and understanding the extent to which these are associated with diminished in-person social interaction could inform public health planning for future pandemics or other disasters. Objective To describe the association between living in a US county with diminished mobility during the COVID-19 pandemic and self-reported depressive symptoms, while accounting for potential local and state-level confounding factors. Design, Setting, and Participants This survey study used 18 waves of a nonprobability internet survey conducted in the United States between May 2020 and April 2022. Participants included respondents who were 18 years and older and lived in 1 of the 50 US states or Washington DC. Main Outcome and Measure Depressive symptoms measured by the Patient Health Questionnaire-9 (PHQ-9); county-level community mobility estimates from mobile apps; COVID-19 policies at the US state level from the Oxford stringency index. Results The 192 271 survey respondents had a mean (SD) of age 43.1 (16.5) years, and 768 (0.4%) were American Indian or Alaska Native individuals, 11 448 (6.0%) were Asian individuals, 20 277 (10.5%) were Black individuals, 15 036 (7.8%) were Hispanic individuals, 1975 (1.0%) were Pacific Islander individuals, 138 702 (72.1%) were White individuals, and 4065 (2.1%) were individuals of another race. Additionally, 126 381 respondents (65.7%) identified as female and 65 890 (34.3%) as male. Mean (SD) depression severity by PHQ-9 was 7.2 (6.8). In a mixed-effects linear regression model, the mean county-level proportion of individuals not leaving home was associated with a greater level of depression symptoms (β, 2.58; 95% CI, 1.57-3.58) after adjustment for individual sociodemographic features. Results were similar after the inclusion in regression models of local COVID-19 activity, weather, and county-level economic features, and persisted after widespread availability of COVID-19 vaccination. They were attenuated by the inclusion of state-level pandemic restrictions. Two restrictions, mandatory mask-wearing in public (β, 0.23; 95% CI, 0.15-0.30) and policies cancelling public events (β, 0.37; 95% CI, 0.22-0.51), demonstrated modest independent associations with depressive symptom severity. Conclusions and Relevance In this study, depressive symptoms were greater in locales and times with diminished community mobility. Strategies to understand the potential public health consequences of pandemic responses are needed

Glycemic Gap Predicts Mortality in a Large Multicenter Cohort Hospitalized With COVID-19.

CONTEXT: Diabetes or hyperglycemia at admission are established risk factors for adverse outcomes during hospitalization for COVID-19, but the impact of prior glycemic control is not clear. OBJECTIVE: We aimed to examine the associations between admission variables, including glycemic gap, and adverse clinical outcomes in patients hospitalized with COVID-19 infection. METHODS: We examined the relationship between clinical predictors, including acute and chronic glycemia, and clinical outcomes, including intensive care unit (ICU) admission, mechanical ventilation (MV), and mortality among 1786 individuals with diabetes or hyperglycemia (glucose > 10 mmol/L twice in 24 hours) who were admitted from March 2020 through February 2021 with COVID-19 infection at 5 university hospitals in the eastern United States. RESULTS: The cohort was 51.3% male, 53.3% White, 18.8% Black, 29.0% Hispanic, with age = 65.6 ± 14.4 years, BMI = 31.5 ± 7.9 kg/m2, glucose = 12.0 ± 7.5 mmol/L [216 ± 135 mg/dL], and HbA1c = 8.07% ± 2.25%. During hospitalization, 38.9% were admitted to the ICU, 22.9% received MV, and 10.6% died. Age (P < 0.001) and admission glucose (P = 0.014) but not HbA1c were associated with increased risk of mortality. Glycemic gap, defined as admission glucose minus estimated average glucose based on HbA1c, was a stronger predictor of mortality than either admission glucose or HbA1c alone (OR = 1.040 [95% CI: 1.019, 1.061] per mmol/L, P < 0.001). In an adjusted multivariable model, glycemic gap, age, BMI, and diabetic ketoacidosis on admission were associated with increased mortality, while higher estimated glomerular filtration rate (eGFR) and use of any diabetes medication were associated with lower mortality (P < 0.001). CONCLUSION: Relative hyperglycemia, as measured by the admission glycemic gap, is an important marker of mortality risk in COVID-19

Anthrax: Evolutionary approaches for genetic-based investigative tools

A TaqMan-minor groove binding assay designed around a nonsense mutation in the plcR gene was used to genotype Bacillus anthracis, B. cereus, and B. thuringiensis isolates. The assay differentiated B. anthracis from these genetic near-neighbors and determined that the nonsense mutation is ubiquitous across 89 globally and genetically diverse B. anthracis strains

Runs of Homozygosity Implicate Autozygosity as a Schizophrenia Risk Factor

Autozygosity occurs when two chromosomal segments that are identical from a common ancestor are inherited from each parent. This occurs at high rates in the offspring of mates who are closely related (inbreeding), but also occurs at lower levels among the offspring of distantly related mates. Here, we use runs of homozygosity in genome-wide SNP data to estimate the proportion of the autosome that exists in autozygous tracts in 9,388 cases with schizophrenia and 12,456 controls. We estimate that the odds of schizophrenia increase by ∼17% for every 1% increase in genome-wide autozygosity. This association is not due to one or a few regions, but results from many autozygous segments spread throughout the genome, and is consistent with a role for multiple recessive or partially recessive alleles in the etiology of schizophrenia. Such a bias towards recessivity suggests that alleles that increase the risk of schizophrenia have been selected against over evolutionary time

Historical Distribution and Molecular Diversity of Bacillus anthracis, Kazakhstan

This study provides useful baseline data for guiding future disease control programs

Recent methods for polygenic analysis of genome-wide data implicate an important effect of common variants on cardiovascular disease risk

<p>Abstract</p> <p>Background</p> <p>Traditional genome-wide association studies are generally limited in their ability explain a large portion of genetic risk for most common diseases. We sought to use both traditional GWAS methods, as well as more recently developed polygenic genome-wide analysis techniques to identify subsets of single-nucleotide polymorphisms (SNPs) that may be involved in risk of cardiovascular disease, as well as estimate the heritability explained by common SNPs.</p> <p>Methods</p> <p>Using data from the Framingham SNP Health Association Resource (SHARe), three complimentary methods were applied to examine the genetic factors associated with the Framingham Risk Score, a widely accepted indicator of underlying cardiovascular disease risk. The first method adopted a traditional GWAS approach - independently testing each SNP for association with the Framingham Risk Score. The second two approaches involved polygenic methods with the intention of providing estimates of aggregate genetic risk and heritability.</p> <p>Results</p> <p>While no SNPs were independently associated with the Framingham Risk Score based on the results of the traditional GWAS analysis, we were able to identify cardiovascular disease-related SNPs as reported by previous studies. A predictive polygenic analysis was only able to explain approximately 1% of the genetic variance when predicting the 10-year risk of general cardiovascular disease. However, 20% to 30% of the variation in the Framingham Risk Score was explained using a recently developed method that considers the joint effect of all SNPs simultaneously.</p> <p>Conclusion</p> <p>The results of this study imply that common SNPs explain a large amount of the variation in the Framingham Risk Score and suggest that future, better-powered genome-wide association studies, possibly informed by knowledge of gene-pathways, will uncover more risk variants that will help to elucidate the genetic architecture of cardiovascular disease.</p

Global Genetic Population Structure of Bacillus anthracis

Anthrax, caused by the bacterium Bacillus anthracis, is a disease of historical and current importance that is found throughout the world. The basis of its historical transmission is anecdotal and its true global population structure has remained largely cryptic. Seven diverse B. anthracis strains were whole-genome sequenced to identify rare single nucleotide polymorphisms (SNPs), followed by phylogenetic reconstruction of these characters onto an evolutionary model. This analysis identified SNPs that define the major clonal lineages within the species. These SNPs, in concert with 15 variable number tandem repeat (VNTR) markers, were used to subtype a collection of 1,033 B. anthracis isolates from 42 countries to create an extensive genotype data set. These analyses subdivided the isolates into three previously recognized major lineages (A, B, and C), with further subdivision into 12 clonal sub-lineages or sub-groups and, finally, 221 unique MLVA15 genotypes. This rare genomic variation was used to document the evolutionary progression of B. anthracis and to establish global patterns of diversity. Isolates in the A lineage are widely dispersed globally, whereas the B and C lineages occur on more restricted spatial scales. Molecular clock models based upon genome-wide synonymous substitutions indicate there was a massive radiation of the A lineage that occurred in the mid-Holocene (3,064–6,127 ybp). On more recent temporal scales, the global population structure of B. anthracis reflects colonial-era importation of specific genotypes from the Old World into the New World, as well as the repeated industrial importation of diverse genotypes into developed countries via spore-contaminated animal products. These findings indicate humans have played an important role in the evolution of anthrax by increasing the proliferation and dispersal of this now global disease. Finally, the value of global genotypic analysis for investigating bioterrorist-mediated outbreaks of anthrax is demonstrated

Detecting autozygosity through runs of homozygosity: A comparison of three autozygosity detection algorithms

<p>Abstract</p> <p>Background</p> <p>A central aim for studying runs of homozygosity (ROHs) in genome-wide SNP data is to detect the effects of autozygosity (stretches of the two homologous chromosomes within the same individual that are identical by descent) on phenotypes. However, it is unknown which current ROH detection program, and which set of parameters within a given program, is optimal for differentiating ROHs that are truly autozygous from ROHs that are homozygous at the marker level but vary at unmeasured variants between the markers.</p> <p>Method</p> <p>We simulated 120 Mb of sequence data in order to know the true state of autozygosity. We then extracted common variants from this sequence to mimic the properties of SNP platforms and performed ROH analyses using three popular ROH detection programs, PLINK, GERMLINE, and BEAGLE. We varied detection thresholds for each program (e.g., prior probabilities, lengths of ROHs) to understand their effects on detecting known autozygosity.</p> <p>Results</p> <p>Within the optimal thresholds for each program, PLINK outperformed GERMLINE and BEAGLE in detecting autozygosity from distant common ancestors. PLINK's sliding window algorithm worked best when using SNP data pruned for linkage disequilibrium (LD).</p> <p>Conclusion</p> <p>Our results provide both general and specific recommendations for maximizing autozygosity detection in genome-wide SNP data, and should apply equally well to research on whole-genome autozygosity burden or to research on whether specific autozygous regions are predictive using association mapping methods.</p

c-Type Cytochrome-Dependent Formation of U(IV) Nanoparticles by Shewanella oneidensis

Modern approaches for bioremediation of radionuclide contaminated environments are based on the ability of microorganisms to effectively catalyze changes in the oxidation states of metals that in turn influence their solubility. Although microbial metal reduction has been identified as an effective means for immobilizing highly-soluble uranium(VI) complexes in situ, the biomolecular mechanisms of U(VI) reduction are not well understood. Here, we show that c-type cytochromes of a dissimilatory metal-reducing bacterium, Shewanella oneidensis MR-1, are essential for the reduction of U(VI) and formation of extracelluar UO (2) nanoparticles. In particular, the outer membrane (OM) decaheme cytochrome MtrC (metal reduction), previously implicated in Mn(IV) and Fe(III) reduction, directly transferred electrons to U(VI). Additionally, deletions of mtrC and/or omcA significantly affected the in vivo U(VI) reduction rate relative to wild-type MR-1. Similar to the wild-type, the mutants accumulated UO (2) nanoparticles extracellularly to high densities in association with an extracellular polymeric substance (EPS). In wild-type cells, this UO (2)-EPS matrix exhibited glycocalyx-like properties and contained multiple elements of the OM, polysaccharide, and heme-containing proteins. Using a novel combination of methods including synchrotron-based X-ray fluorescence microscopy and high-resolution immune-electron microscopy, we demonstrate a close association of the extracellular UO (2) nanoparticles with MtrC and OmcA (outer membrane cytochrome). This is the first study to our knowledge to directly localize the OM-associated cytochromes with EPS, which contains biogenic UO (2) nanoparticles. In the environment, such association of UO (2) nanoparticles with biopolymers may exert a strong influence on subsequent behavior including susceptibility to oxidation by O (2) or transport in soils and sediments