Nat Chem Biol. 2023 Feb 20. doi: 10.1038/s41589-023-01269-7. Online ahead of print.

ABSTRACT

Single-strand RNA (ssRNA) Fiersviridae phages cause host lysis with a product of single gene (sgl for single-gene lysis; product Sgl) that induces autolysis. Many different Sgls have been discovered, but the molecular targets of only a few have been identified. In this study, we used a high-throughput genetic screen to uncover genome-wide host suppressors of diverse Sgls. In addition to validating known molecular mechanisms, we discovered that the Sgl of PP7, an ssRNA phage of Pseudomonas aeruginosa, targets MurJ, the flippase responsible for lipid II export, previously shown to be the target of the Sgl of coliphage M. These two Sgls, which are unrelated and predicted to have opposite membrane topology, thus represent a case of convergent evolution. We extended the genetic screens to other uncharacterized Sgls and uncovered a common set of multicopy suppressors, suggesting that these Sgls act by the same or similar mechanism.

PMID:36805702 | DOI:10.1038/s41589-023-01269-7

Nat Biotechnol. 2023 Feb 20. doi: 10.1038/s41587-023-01674-2. Online ahead of print.

ABSTRACT

Pure bacterial cultures remain essential for detailed experimental and mechanistic studies in microbiome research, and traditional methods to isolate individual bacteria from complex microbial ecosystems are labor-intensive, difficult-to-scale and lack phenotype-genotype integration. Here we describe an open-source high-throughput robotic strain isolation platform for the rapid generation of isolates on demand. We develop a machine learning approach that leverages colony morphology and genomic data to maximize the diversity of microbes isolated and enable targeted picking of specific genera. Application of this platform on fecal samples from 20 humans yields personalized gut microbiome biobanks totaling 26,997 isolates that represented >80% of all abundant taxa. Spatial analysis on >100,000 visually captured colonies reveals cogrowth patterns between Ruminococcaceae, Bacteroidaceae, Coriobacteriaceae and Bifidobacteriaceae families that suggest important microbial interactions. Comparative analysis of 1,197 high-quality genomes from these biobanks shows interesting intra- and interpersonal strain evolution, selection and horizontal gene transfer. This culturomics framework should empower new research efforts to systematize the collection and quantitative analysis of imaging-based phenotypes with high-resolution genomics data for many emerging microbiome studies.

PMID:36805559 | DOI:10.1038/s41587-023-01674-2

Genomics Proteomics Bioinformatics. 2023 Feb 16:S1672-0229(23)00038-4. doi: 10.1016/j.gpb.2023.01.007. Online ahead of print.

ABSTRACT

Circular RNAs (circRNAs) are endogenous non-coding RNAs with covalently closed structures, which have important functions in plants. However, their biogenesis, degradation, and function upon treatment with gibberellins (GAs) and auxins (1-Naphthaleneacetic acid, NAA) remain unknown. Here, we systematically identified and characterized expression patterns, evolutionary conservation, genomic features, and internal structures of circRNAs using RNase R-treated libraries from moso bamboo (Phyllostachys edulis) seedlings. Moreover, we investigated the biogenesis of circRNAs dependent on both cis- and trans-regulation. We explored the function of circRNAs, including their roles in regulating microRNA-related genes and modulating the alternative splicing of their linear counterparts. Importantly, we developed a customized degradome sequencing approach to detect microRNA-mediated cleavage of circRNAs. Finally, we presented a comprehensive view of the participation of circRNAs in the regulation of hormone metabolism upon treatment of bamboo seedlings with GA and NAA. Collectively, our study gives insights into biogenesis, function, and microRNA-mediated degradation of circRNAs in moso bamboo.

PMID:36805531 | DOI:10.1016/j.gpb.2023.01.007

Environ Geochem Health. 2023 Feb 20. doi: 10.1007/s10653-023-01510-1. Online ahead of print.

ABSTRACT

This study aimed to determine the common latent patterns of geographical distribution of health-related minerals across the USA and to evaluate the real-world cumulative effects of these patterns on overall population health. It was an ecological study using county-level data (3080 contiguous counties) on the concentrations of 14 minerals (i.e., aluminum, arsenic, calcium, copper, iron, lead, magnesium, manganese, mercury, phosphorus, selenium, sodium, titanium, zinc) in stream sediments (or surface soils), and the measurements of overall health including life expectancy at birth, age-specific mortality risks and cause-specific (summarized by 21 mutually exclusive groups) mortality rates. Latent class analysis (LCA) was employed to identify the common clusters of life expectancy-related minerals based on their concentration characteristics. Multivariate linear regression analyses were then conducted to examine the relationship between the LCA-derived clusters and the health measurements, with adjustment for potential confounding factors. Five minerals (i.e., arsenic, calcium, selenium, sodium and zinc) were associated with life expectancy and were analyzed in LCA. Three clusters were determined across the USA, the 'common' (n = 2056, 66.8%), 'infertile' (n = 739, 24.0%) and 'plentiful' (n = 285, 9.3%) clusters. Residents in counties with the 'infertile' profile were associated with the shortest life expectancy, highest mortality risks at all ages, and highest mortality rates for many reasons including the top five leading causes of death: cardiovascular diseases, neoplasms, neurological disorders, chronic respiratory conditions, and diabetes, urogenital, blood and endocrine diseases. Results remained statistically significant after confounding adjustment. Our study brings novel perspectives regarding environmental geochemistry to explain health disparities in the USA.

PMID:36805365 | DOI:10.1007/s10653-023-01510-1

Int J Infect Dis. 2023 Feb 15:S1201-9712(23)00050-4. doi: 10.1016/j.ijid.2023.02.005. Online ahead of print.

ABSTRACT

OBJECTIVES: We determined pulse oximetry benefit in pediatric pneumonia mortality-risk stratification and chest indrawing pneumonia in-hospital mortality risk factors.

METHODS: We report characteristics and in-hospital pneumonia-related mortality of children 2-59-months-old included in the Pneumonia Research Partnership to Assess WHO Recommendations dataset. We developed multivariable logistic regression models of chest indrawing pneumonia to identify mortality risk factors.

RESULTS: Among 285,839 children, 164,244 (57·5%) from hospital-based studies were included. Pneumonia case fatality risk (CFR) without pulse oximetry measurement was higher than with measurement (5·8%, 95% CI 5·6-5·9% vs 2·1%, 95% CI 1·9-2·4%). One in five children with chest indrawing pneumonia was hypoxemic (19·7%, 95% CI 19·0-20·4%) and the hypoxemic CFR was 10·3% (95% CI 9·1%-11·5%). Other mortality risk factors were younger age (either 2-5 months (aOR 9·94, 95% CI 6·67-14·84) or 6-11 months (aOR 2·67, 95% CI 1·71-4·16)), moderate malnutrition (aOR 2·41, 95% CI 1·87-3·09), and female sex (aOR 1·82, 95% CI 1·43-2·32).

CONCLUSIONS: Children with a pulse oximetry measurement had a lower CFR. Many children hospitalized with chest indrawing pneumonia were hypoxemic and one in ten died. Young age and moderate malnutrition were risk factors for in-hospital chest indrawing pneumonia-related mortality. Pulse oximetry should be integrated in under-five pneumonia hospital care.

PMID:36805325 | DOI:10.1016/j.ijid.2023.02.005

Biosens Bioelectron. 2023 Feb 15;227:115152. doi: 10.1016/j.bios.2023.115152. Online ahead of print.

ABSTRACT

Multiple studies showed that metabolic disorders play a critical role in respiratory infectious diseases, including COVID-19. Metabolites contained in small extracellular vesicles (sEVs) are different from those in plasma at the acute stage, while the metabolic features of plasma sEVs of COVID-19 survivors remain unknown. Here, we used a nanopore membrane-based microfluidic chip for plasma sEVs separation, termed ExoSEC, and compared the sEVs obtained by UC, REG, and ExoSEC in terms the time, cost, purity, and metabolic features. The results indicated the ExoSEC was much less costly, provided higher purity by particles/proteins ratio, and achieved 205-fold and 2-fold higher sEVs yield, than UC and REG, respectively. Moreover, more metabolites were identified and several signaling pathways were significantly enriched in ExoSEC-sEVs compared to UC-sEVs and REG-sEVs. Furthermore, we detected 306 metabolites in plasma sEVs using ExoSEC from recovered asymptomatic (RA), moderate (RM), and severe/critical COVID-19 (RS) patients without underlying diseases 3 months after discharge. Our study demonstrated that COVID-19 survivors, especially RS, experienced significant metabolic alteration and the dysregulated pathways mainly involved fatty acid biosynthesis, phenylalanine metabolism, etc. Metabolites of the fatty acid biosynthesis pathway bore a significantly negative association with red blood cell counts and hemoglobin, which might be ascribed to hypoxia or respiratory failure in RM and RS but not in RA at the acute stage. Our study confirmed that ExoSEC could provide a practical and economical alternative for high throughput sEVs metabolomic study.

PMID:36805272 | DOI:10.1016/j.bios.2023.115152

Environ Microbiome. 2023 Feb 19;18(1):10. doi: 10.1186/s40793-023-00470-4.

ABSTRACT

BACKGROUND: Microorganisms such as coliform-forming bacteria are commonly used to assess freshwater quality for drinking and recreational use. However, such organisms do not exist in isolation; they exist within the context of dynamic, interactive microbial communities which vary through space and time. Elucidating spatiotemporal microbial dynamics is imperative for discriminating robust community changes from ephemeral ecological trends, and for improving our overall understanding of the relationship between microbial communities and ecosystem health. We conducted a seven-year (2013-2019) microbial time-series investigation in the Chicago Area Waterways (CAWS): an urban river system which, in 2016, experienced substantial upgrades to disinfection processes at two wastewater reclamation plants (WRPs) that discharge into the CAWS and improved stormwater capture, to improve river water quality and reduce flooding. Using culture-independent and culture-dependent approaches, we compared CAWS microbial ecology before and after the intervention.

RESULTS: Examinations of time-resolved beta distances between WRP-adjacent sites showed that community similarity measures were often consistent with the spatial orientation of site locations to one another and to the WRP outfalls. Fecal coliform results suggested that upgrades reduced coliform-associated bacteria in the effluent and the downstream river community. However, examinations of whole community changes through time suggest that the upgrades did little to affect overall riverine community dynamics, which instead were overwhelmingly driven by yearly patterns consistent with seasonality.

CONCLUSIONS: This study presents a systematic effort to combine 16S rRNA gene amplicon sequencing with traditional culture-based methods to evaluate the influence of treatment innovations and systems upgrades on the microbiome of the Chicago Area Waterway System, representing the longest and most comprehensive characterization of the microbiome of an urban waterway yet attempted. We found that the systems upgrades were successful in improving specific water quality measures immediately downstream of wastewater outflows. Additionally, we found that the implementation of the water quality improvement measures to the river system did not disrupt the overall dynamics of the downstream microbial community, which remained heavily influenced by seasonal trends. Such results emphasize the dynamic nature of microbiomes in open environmental systems such as the CAWS, but also suggest that the seasonal oscillations remain consistent even when perturbed.

PMID:36805022 | DOI:10.1186/s40793-023-00470-4

Mol Cell. 2023 Feb 16;83(4):503-506. doi: 10.1016/j.molcel.2023.02.001.

ABSTRACT

Adachi et al.1 and Song et al.2 demonstrate the feasibility of engineering pseudouridylation at specific sites and its utility to correct disease-causing premature termination codons (PTCs) in human cells.

PMID:36804913 | DOI:10.1016/j.molcel.2023.02.001

Chem Commun (Camb). 2023 Feb 21. doi: 10.1039/d2cc06950d. Online ahead of print.

ABSTRACT

Ginsenoside Rg1, a tetracyclic triterpenoid derivative extracted from the roots of Panax ginseng C. A. Meyer, can enhance learning and memory and improve cognitive impairment. However, whether or how it affects vesicular dopamine storage and its release during exocytosis remains unknown. By using single-vesicle electrochemistry, we for the first time find out that Rg1 not only upregulates vesicular dopamine content but also increases exocytosis frequency and modulates dopamine release during exocytosis in PC12 cells, which may relate to the activation of protein kinases, causing a series of biological cascades. This finding offers the possible link between Rg1 and vesicular chemical storage and exocytotic release, which is of significance for understanding the nootropic role of Rg1 from the perspective of neurotransmission.

PMID:36804575 | DOI:10.1039/d2cc06950d

Trends Cancer. 2023 Feb 18:S2405-8033(23)00007-9. doi: 10.1016/j.trecan.2023.01.005. Online ahead of print.

ABSTRACT

Cancer cells undergo metabolic reprogramming to rely mostly on aerobic glycolysis (the Warburg effect). The increased glycolytic intake enhances the intracellular levels of reactive sugars and sugar metabolites. These reactive species can covalently modify macromolecules in a process termed glycation. Histones are particularly susceptible to glycation, resulting in substantial alterations to chromatin structure, function, and transcriptional output. Growing evidence suggests a link between dysregulated metabolism of tumors and cancer proliferation through epigenetic changes. This review discusses recent advances in the understanding of histone glycation, its impact on the epigenetic landscape and cellular fate, and its role in cancer. In addition, we investigate the possibility of using histone glycation as biomarkers and targets for anticancer therapeutics.

PMID:36804508 | DOI:10.1016/j.trecan.2023.01.005

Am J Transplant. 2023 Feb;23(2):272-277. doi: 10.1016/j.ajt.2022.11.011. Epub 2023 Jan 12.

ABSTRACT

Intestinal commensals can exert immunomodulatory effects on the host, with beneficial or detrimental consequences depending on underlying diseases. We have previously correlated longer survival of minor mismatched skin grafts in mice with the presence of an intestinal commensal bacterium, Alistipes onderdonkii. In this study, we investigated its sufficiency and mechanism of action. Oral administration of A onderdonkii strain DSM19147 but not DSM108265 was sufficient to prolong minor mismatched skin graft survival through inhibition of tumor necrosis factor production. Through metabolomic and metagenomic comparisons between DSM19147 and DSM108265, we identified candidate gene products associated with the anti-inflammatory effect of DSM19147. A onderdonkii DSM19147 can lower inflammation both at a steady state and after transplantation and may serve as an anti-inflammatory probiotic beneficial for transplant recipients.

PMID:36804134 | DOI:10.1016/j.ajt.2022.11.011

J Biosci Bioeng. 2023 Feb 16:S1389-1723(23)00027-0. doi: 10.1016/j.jbiosc.2023.01.010. Online ahead of print.

ABSTRACT

Saccharomyces cerevisiae has a long-standing history of biotechnological applications even before the dawn of modern biotechnology. The field is undergoing accelerated advancement with the recent systems and synthetic biology approaches. In this review, we highlight the recent findings in the field with a focus on omics studies of S. cerevisiae to investigate its stress tolerance in different industries. The latest advancements in S. cerevisiae systems and synthetic biology approaches for the development of genome-scale metabolic models (GEMs) and molecular tools such as multiplex Cas9, Cas12a, Cpf1, and Csy4 genome editing tools, modular expression cassette with optimal transcription factors, promoters, and terminator libraries as well as metabolic engineering. Omics data analysis is key to the identification of exploitable native genes/proteins/pathways in S. cerevisiae with the optimization of heterologous pathway implementation and fermentation conditions. Through systems and synthetic biology, various heterologous compound productions that require non-native biosynthetic pathways in a cell factory have been established via different strategies of metabolic engineering integrated with machine learning.

PMID:36803862 | DOI:10.1016/j.jbiosc.2023.01.010

Clin Epigenetics. 2023 Feb 20;15(1):28. doi: 10.1186/s13148-023-01429-5.

ABSTRACT

BACKGROUND: Intraductal papillary mucinous neoplasms (IPMNs), a type of cystic pancreatic cancer (PC) precursors, are increasingly identified on cross-sectional imaging and present a significant diagnostic challenge. While surgical resection of IPMN-related advanced neoplasia, i.e., IPMN-related high-grade dysplasia or PC, is an essential early PC detection strategy, resection is not recommended for IPMN-low-grade dysplasia (LGD) due to minimal risk of carcinogenesis, and significant procedural risks. Based on their promising results in prior validation studies targeting early detection of classical PC, DNA hypermethylation-based markers may serve as a biomarker for malignant risk stratification of IPMNs. This study investigates our DNA methylation-based PC biomarker panel (ADAMTS1, BNC1, and CACNA1G genes) in differentiating IPMN-advanced neoplasia from IPMN-LGDs.

METHODS: Our previously described genome-wide pharmaco-epigenetic method identified multiple genes as potential targets for PC detection. The combination was further optimized and validated for early detection of classical PC in previous case-control studies. These promising genes were evaluated among micro-dissected IPMN tissue (IPMN-LGD: 35, IPMN-advanced neoplasia: 35) through Methylation-Specific PCR. The discriminant capacity of individual and combination of genes were delineated through Receiver Operating Characteristics curve analysis.

RESULTS: As compared to IPMN-LGDs, IPMN-advanced neoplasia had higher hypermethylation frequency of candidate genes: ADAMTS1 (60% vs. 14%), BNC1 (66% vs. 3%), and CACGNA1G (25% vs. 0%). We observed Area Under Curve (AUC) values of 0.73 for ADAMTS1, 0.81 for BNC1, and 0.63 for CACNA1G genes. The combination of the BNC1/ CACNA1G genes resulted in an AUC of 0.84, sensitivity of 71%, and specificity of 97%. Combining the methylation status of the BNC1/CACNA1G genes, blood-based CA19-9, and IPMN lesion size enhanced the AUC to 0.92.

CONCLUSION: DNA-methylation based biomarkers have shown a high diagnostic specificity and moderate sensitivity for differentiating IPMN-advanced neoplasia from LGDs. Addition of specific methylation targets can improve the accuracy of the methylation biomarker panel and enable the development of noninvasive IPMN stratification biomarkers.

PMID:36803844 | DOI:10.1186/s13148-023-01429-5

Biomater Res. 2023 Feb 17;27(1):15. doi: 10.1186/s40824-023-00354-1.

ABSTRACT

Revealing the spatiotemporal behavior of DNA double-strand breaks (DSBs) is crucial for understanding the processes of DNA damage and repair. Traditionally, γH2AX and DNA damage response (DDR) factors have been used to detect DSBs using classical biochemical assays, such as antibody-based immunostaining. However, a reliable method to visualize and assess DSB activity real-time in living cells is yet to be established. Herein, we developed a novel DNA double-strand breaks biosensor (DSBS) based on fluorescence resonance energy transfer (FRET) by employing the H2AX and BRCT1 domains. By applying FRET imaging with DSBS, we show that DSBS specifically reacts to drug- or ionizing radiation (IR)-induced γH2AX activity, allowing for the quantification of DSB events at high spatiotemporal resolutions. Taken together, we provide a new experimental tool to evaluate the spatiotemporal dynamics of DNA double-strand breaks. Ultimately, our biosensor can be useful for elucidating the molecular mechanisms underlying DNA damage and repair processes.

PMID:36803668 | DOI:10.1186/s40824-023-00354-1

Elife. 2023 Feb 20;12:e69415. doi: 10.7554/eLife.69415. Online ahead of print.

ABSTRACT

Glycan-binding proteins, so-called lectins, are exposed on mammalian cell surfaces and decipher the information encoded within glycans translating it into biochemical signal transduction pathways in the cell. These glycan-lectin communication pathways are complex and difficult to analyze. However, quantitative data with single cell resolution provide means to disentangle the associated signaling cascades. We chose C-type lectin receptors (CTLs) expressed on immune cells as a model system to study their capacity to transmit information encoded in glycans of incoming particles. In particular, we used NF-κB-reporter cell lines expressing DC-SIGN, MCL, dectin-1, dectin-2, and mincle, as well as TNFαR and TLR-1&2 in monocytic cell lines and compared their transmission of glycan-encoded information. All receptors did transmit information with similar signaling capacity, except dectin-2. This lectin was identified to less efficient in information transmission compared to the other CTLs and even while the sensitivity of the dectin-2 pathway was enhanced by overexpression of its co-receptor FcRγ, its transmitted information was not. Next, we expanded our investigation towards the integration of multiple signal transduction pathways including synergistic lectins, which is crucial during pathogen recognition. We show how the signaling capacity of lectin receptors using a similar signal transduction pathway (dectin-1 and dectin-2) are being integrated by compromising between the lectins. In contrast, co-expression of MCL synergistically enhanced the dectin-2 signaling capacity, particularly at low glycan stimulant concentration. By using dectin-2 and other lectins as examples, we demonstrate how signaling capacity of dectin-2 is modulated in the presence of other lectins and therefore the findings provide insight into how immune cells translate glycan information using multivalent interactions.

PMID:36803584 | DOI:10.7554/eLife.69415

BMC Bioinformatics. 2023 Feb 17;24(1):53. doi: 10.1186/s12859-023-05144-z.

ABSTRACT

BACKGROUND: Bacterial and viral infections may cause or exacerbate various human diseases and to detect microbes in tissue, one method of choice is RNA sequencing. The detection of specific microbes using RNA sequencing offers good sensitivity and specificity, but untargeted approaches suffer from high false positive rates and a lack of sensitivity for lowly abundant organisms.

RESULTS: We introduce Pathonoia, an algorithm that detects viruses and bacteria in RNA sequencing data with high precision and recall. Pathonoia first applies an established k-mer based method for species identification and then aggregates this evidence over all reads in a sample. In addition, we provide an easy-to-use analysis framework that highlights potential microbe-host interactions by correlating the microbial to the host gene expression. Pathonoia outperforms state-of-the-art methods in microbial detection specificity, both on in silico and real datasets.

CONCLUSION: Two case studies in human liver and brain show how Pathonoia can support novel hypotheses on microbial infection exacerbating disease. The Python package for Pathonoia sample analysis and a guided analysis Jupyter notebook for bulk RNAseq datasets are available on GitHub.

PMID:36803415 | DOI:10.1186/s12859-023-05144-z

BMC Bioinformatics. 2023 Feb 20;24(1):57. doi: 10.1186/s12859-023-05171-w.

ABSTRACT

BACKGROUND: The growing amount of high dimensional biomolecular data has spawned new statistical and computational models for risk prediction and disease classification. Yet, many of these methods do not yield biologically interpretable models, despite offering high classification accuracy. An exception, the top-scoring pair (TSP) algorithm derives parameter-free, biologically interpretable single pair decision rules that are accurate and robust in disease classification. However, standard TSP methods do not accommodate covariates that could heavily influence feature selection for the top-scoring pair. Herein, we propose a covariate-adjusted TSP method, which uses residuals from a regression of features on the covariates for identifying top scoring pairs. We conduct simulations and a data application to investigate our method, and compare it to existing classifiers, LASSO and random forests.

RESULTS: Our simulations found that features that were highly correlated with clinical variables had high likelihood of being selected as top scoring pairs in the standard TSP setting. However, through residualization, our covariate-adjusted TSP was able to identify new top scoring pairs, that were largely uncorrelated with clinical variables. In the data application, using patients with diabetes (n = 977) selected for metabolomic profiling in the Chronic Renal Insufficiency Cohort (CRIC) study, the standard TSP algorithm identified (valine-betaine, dimethyl-arg) as the top-scoring metabolite pair for classifying diabetic kidney disease (DKD) severity, whereas the covariate-adjusted TSP method identified the pair (pipazethate, octaethylene glycol) as top-scoring. Valine-betaine and dimethyl-arg had, respectively, ≥ 0.4 absolute correlation with urine albumin and serum creatinine, known prognosticators of DKD. Thus without covariate-adjustment the top-scoring pair largely reflected known markers of disease severity, whereas covariate-adjusted TSP uncovered features liberated from confounding, and identified independent prognostic markers of DKD severity. Furthermore, TSP-based methods achieved competitive classification accuracy in DKD to LASSO and random forests, while providing more parsimonious models.

CONCLUSIONS: We extended TSP-based methods to account for covariates, via a simple, easy to implement residualizing process. Our covariate-adjusted TSP method identified metabolite features, uncorrelated from clinical covariates, that discriminate DKD severity stage based on the relative ordering between two features, and thus provide insights into future studies on the order reversals in early vs advanced disease states.

PMID:36803209 | DOI:10.1186/s12859-023-05171-w

Circ Genom Precis Med. 2023 Feb 21:e003791. doi: 10.1161/CIRCGEN.122.003791. Online ahead of print.

ABSTRACT

BACKGROUND: Congenital heart disease (CHD) is the most common major congenital anomaly and causes significant morbidity and mortality. Epidemiologic evidence supports a role of genetics in the development of CHD. Genetic diagnoses can inform prognosis and clinical management. However, genetic testing is not standardized among individuals with CHD. We sought to develop a list of validated CHD genes using established methods and to evaluate the process of returning genetic results to research participants in a large genomic study.

METHODS: Two-hundred ninety-five candidate CHD genes were evaluated using a ClinGen framework. Sequence and copy number variants involving genes in the CHD gene list were analyzed in Pediatric Cardiac Genomics Consortium participants. Pathogenic/likely pathogenic results were confirmed on a new sample in a clinical laboratory improvement amendments-certified laboratory and disclosed to eligible participants. Adult probands and parents of probands who received results were asked to complete a post-disclosure survey.

RESULTS: A total of 99 genes had a strong or definitive clinical validity classification. Diagnostic yields for copy number variants and exome sequencing were 1.8% and 3.8%, respectively. Thirty-one probands completed clinical laboratory improvement amendments-confirmation and received results. Participants who completed postdisclosure surveys reported high personal utility and no decision regret after receiving genetic results.

CONCLUSIONS: The application of ClinGen criteria to CHD candidate genes yielded a list that can be used to interpret clinical genetic testing for CHD. Applying this gene list to one of the largest research cohorts of CHD participants provides a lower bound for the yield of genetic testing in CHD.

PMID:36803080 | DOI:10.1161/CIRCGEN.122.003791

PLoS Comput Biol. 2023 Feb 21;19(2):e1010890. doi: 10.1371/journal.pcbi.1010890. Online ahead of print.

ABSTRACT

Causal interactions and correlations between clinically-relevant biomarkers are important to understand, both for informing potential medical interventions as well as predicting the likely health trajectory of any individual as they age. These interactions and correlations can be hard to establish in humans, due to the difficulties of routine sampling and controlling for individual differences (e.g., diet, socio-economic status, medication). Because bottlenose dolphins are long-lived mammals that exhibit several age-related phenomena similar to humans, we analyzed data from a well controlled 25-year longitudinal cohort of 144 dolphins. The data from this study has been reported on earlier, and consists of 44 clinically relevant biomarkers. This time-series data exhibits three starkly different influences: (A) directed interactions between biomarkers, (B) sources of biological variation that can either correlate or decorrelate different biomarkers, and (C) random observation-noise which combines measurement error and very rapid fluctuations in the dolphin's biomarkers. Importantly, the sources of biological variation (type-B) are large in magnitude, often comparable to the observation errors (type-C) and larger than the effect of the directed interactions (type-A). Attempting to recover the type-A interactions without accounting for the type-B and type-C variation can result in an abundance of false-positives and false-negatives. Using a generalized regression which fits the longitudinal data with a linear model accounting for all three influences, we demonstrate that the dolphins exhibit many significant directed interactions (type-A), as well as strong correlated variation (type-B), between several pairs of biomarkers. Moreover, many of these interactions are associated with advanced age, suggesting that these interactions can be monitored and/or targeted to predict and potentially affect aging.

PMID:36802395 | DOI:10.1371/journal.pcbi.1010890

Appl Environ Microbiol. 2023 Feb 21:e0008223. doi: 10.1128/aem.00082-23. Online ahead of print.

ABSTRACT

Some probiotic bifidobacteria are highly robust and shelf-stable, whereas others are difficult to produce, due to their sensitivity to stressors. This limits their potential use as probiotics. Here, we investigate the molecular mechanisms underlying the variability in stress physiologies of Bifidobacterium animalis subsp. lactis BB-12 and Bifidobacterium longum subsp. longum BB-46, by applying a combination of classical physiological characterization and transcriptome profiling. The growth behavior, metabolite production, and global gene expression profiles differed considerably between the strains. BB-12 consistently showed higher expression levels of multiple stress-associated genes, compared to BB-46. This difference, besides higher cell surface hydrophobicity and a lower ratio of unsaturated to saturated fatty acids in the cell membrane of BB-12, should contribute to its higher robustness and stability. In BB-46, the expression of genes related to DNA repair and fatty acid biosynthesis was higher in the stationary than in the exponential phase, which was associated with enhanced stability of BB-46 cells harvested in the stationary phase. The results presented herein highlight important genomic and physiological features contributing to the stability and robustness of the studied Bifidobacterium strains. IMPORTANCE Probiotics are industrially and clinically important microorganisms. To exert their health-promoting effects, probiotic microorganisms must be administered at high counts, while maintaining their viability at the time of consumption. In addition, intestinal survival and bioactivity are important criteria for probiotics. Although bifidobacteria are among the most well-documented probiotics, the industrial-scale production and commercialization of some Bifidobacterium strains is challenged by their high sensitivity to environmental stressors encountered during manufacturing and storage. Through a comprehensive comparison of the metabolic and physiological characteristics of 2 Bifidobacterium strains, we identify key biological markers that can serve as indicators for robustness and stability in bifidobacteria.

PMID:36802222 | DOI:10.1128/aem.00082-23