PLoS One. 2023 Mar 13;18(3):e0281900. doi: 10.1371/journal.pone.0281900. eCollection 2023.

ABSTRACT

Machine learning (ML) algorithms to detect critical findings on head CTs may expedite patient management. Most ML algorithms for diagnostic imaging analysis utilize dichotomous classifications to determine whether a specific abnormality is present. However, imaging findings may be indeterminate, and algorithmic inferences may have substantial uncertainty. We incorporated awareness of uncertainty into an ML algorithm that detects intracranial hemorrhage or other urgent intracranial abnormalities and evaluated prospectively identified, 1000 consecutive noncontrast head CTs assigned to Emergency Department Neuroradiology for interpretation. The algorithm classified the scans into high (IC+) and low (IC-) probabilities for intracranial hemorrhage or other urgent abnormalities. All other cases were designated as No Prediction (NP) by the algorithm. The positive predictive value for IC+ cases (N = 103) was 0.91 (CI: 0.84-0.96), and the negative predictive value for IC- cases (N = 729) was 0.94 (0.91-0.96). Admission, neurosurgical intervention, and 30-day mortality rates for IC+ was 75% (63-84), 35% (24-47), and 10% (4-20), compared to 43% (40-47), 4% (3-6), and 3% (2-5) for IC-. There were 168 NP cases, of which 32% had intracranial hemorrhage or other urgent abnormalities, 31% had artifacts and postoperative changes, and 29% had no abnormalities. An ML algorithm incorporating uncertainty classified most head CTs into clinically relevant groups with high predictive values and may help accelerate the management of patients with intracranial hemorrhage or other urgent intracranial abnormalities.

PMID:36913348 | DOI:10.1371/journal.pone.0281900

JAMA Pediatr. 2023 Mar 13. doi: 10.1001/jamapediatrics.2023.0053. Online ahead of print.

ABSTRACT

IMPORTANCE: Maternal hepatitis B virus (HBV) infection during early pregnancy has been related to congenital heart diseases (CHDs) in offspring. However, no study to date has evaluated the association of maternal preconception HBV infection with CHDs in offspring.

OBJECTIVE: To explore the association of maternal preconception HBV infection with CHDs in offspring.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used nearest-neighbor (1:4) propensity score matching of 2013 to 2019 data from the National Free Preconception Checkup Project (NFPCP), a national free health service for childbearing-aged women who plan to conceive throughout mainland China. Women aged 20 to 49 years who got pregnant within 1 year after preconception examination were included, and those with multiple births were excluded. Data were analyzed from September to December 2022.

EXPOSURES: Maternal preconception HBV infection statuses, including uninfected, previous, and new infection.

MAIN OUTCOMES AND MEASURES: The main outcome was CHDs, which were prospectively collected from the birth defect registration card of the NFPCP. Logistic regression with robust error variances was used to estimate the association between maternal preconception HBV infection status and CHD risk in offspring, after adjusting for confounding variables.

RESULTS: After matching with a 1:4 ratio, there were 3 690 427 participants included in the final analysis, where 738 945 women were infected with HBV, including 393 332 women with previous infection and 345 613 women with new infection. Approximately 0.03% (800 of 2 951 482) of women uninfected with HBV preconception and women newly infected with HBV carried an infant with CHDs, whereas 0.04% (141 of 393 332) of women with HBV infection prior to pregnancy carried an infant with CHDs. After multivariable adjustment, women with HBV infection prior to pregnancy had a higher risk of CHDs in offspring compared with women who were uninfected (adjusted relative risk ratio [aRR], 1.23; 95% CI, 1.02-1.49). Moreover, compared with couples who were uninfected with HBV prior to pregnancy (680 of 2 610 968 [0.026%]), previously infected women with uninfected men (93 of 252 919 [0.037%]) or previously infected men with uninfected women (43 of 95 735 [0.045%]) had a higher incidence of CHDs in offspring and were significantly associated with a higher risk of CHDs in offspring (previously infected women with uninfected men: aRR, 1.36; 95% CI, 1.09-1.69; previously infected men with uninfected women: aRR, 1.51; 95% CI, 1.09-2.09) with multivariable adjustment, while no significant association was observed between maternal new HBV infection and CHDs in offspring.

CONCLUSIONS AND RELEVANCE: In this matched retrospective cohort study, maternal preconception previous HBV infection was significantly associated with CHDs in offspring. Moreover, among women with HBV-uninfected husbands, significantly increased risk of CHDs was also observed in previously infected women prior to pregnancy. Consequently, HBV screening and getting HBV vaccination-induced immunity for couples prior to pregnancy are indispensable, and those with previous HBV infection prior to pregnancy should also be taken seriously to decrease the CHDs risk in offspring.

PMID:36912830 | DOI:10.1001/jamapediatrics.2023.0053

Expert Rev Gastroenterol Hepatol. 2023 Mar 13. doi: 10.1080/17474124.2023.2191190. Online ahead of print.

ABSTRACT

INTRODUCTION: Non-alcoholic fatty liver disease (NAFLD) is a complex disorder resulting from intricate relationships with diverse cardiometabolic risk factors and environmental factors. NAFLD may result in severe chronic liver damage and potentially declining liver function.

AREAS COVERED: : Accumulated knowledge over the last decade indicates that the disease trajectory presents substantial heterogeneity. In addition, overlapping features with the diseases of the metabolic syndrome, combined with heterogeneity in disease mechanisms, further complicates NAFLD diagnosis and prognosis, and hampers progress in biomarker and pharmacological discoveries. Here, we explore solving the heterogeneous clinical landscape of NAFLD by cluster analysis of molecular signatures that serve as a proxy for disease stratification into molecular sub-types. First, we collected information on NAFLD and metabolic syndrome-associated protein-coding genes by data mining the literature. Next, we performed pathways enrichment and cluster analyses to decipher and dissect the different patterns of phenotypic heterogeneity. Our approach showed unique biological pathways for every clinical subtype/group, namely NAFLD + obesity, NAFLD + arterial hypertension, NAFLD + dyslipidemia, and NAFLD + type 2 diabetes.

EXPERT OPINION: Patients with NAFLD may be benefited by a better understanding of the disease biology, which involves "dissection" of the molecular sub-phenotypes that drive the disease progression.

PMID:36912694 | DOI:10.1080/17474124.2023.2191190

mSystems. 2023 Mar 13:e0081622. doi: 10.1128/msystems.00816-22. Online ahead of print.

ABSTRACT

The scale of post-transcriptional regulation and the implications of its interplay with other forms of regulation in environmental acclimation are underexplored for organisms of the domain Archaea. Here, we have investigated the scale of post-transcriptional regulation in the extremely halophilic archaeon Halobacterium salinarum NRC-1 by integrating the transcriptome-wide locations of transcript processing sites (TPSs) and SmAP1 binding, the genome-wide locations of antisense RNAs (asRNAs), and the consequences of RNase_2099C knockout on the differential expression of all genes. This integrated analysis has discovered that 54% of all protein-coding genes in the genome of this haloarchaeon are likely targeted by multiple mechanisms for putative post-transcriptional processing and regulation, with about 20% of genes likely being regulated by combinatorial schemes involving SmAP1, asRNAs, and RNase_2099C. Comparative analysis of mRNA levels (transcriptome sequencing [RNA-Seq]) and protein levels (sequential window acquisition of all theoretical fragment ion spectra mass spectrometry [SWATH-MS]) for 2,579 genes over four phases of batch culture growth in complex medium generated additional evidence for the conditional post-transcriptional regulation of 7% of all protein-coding genes. We demonstrate that post-transcriptional regulation may act to fine-tune specialized and rapid acclimation to stressful environments, e.g., as a switch to turn on gas vesicle biogenesis to promote vertical relocation under anoxic conditions and modulate the frequency of transposition by insertion sequence (IS) elements of the IS200/IS605, IS4, and ISH3 families. Findings from this study are provided as an atlas in a public Web resource (https://halodata.systemsbiology.net). IMPORTANCE While the transcriptional regulation landscape of archaea has been extensively investigated, we currently have limited knowledge about post-transcriptional regulation and its driving mechanisms in this domain of life. In this study, we collected and integrated omics data from multiple sources and technologies to infer post-transcriptionally regulated genes and the putative mechanisms modulating their expression at the protein level in Halobacterium salinarum NRC-1. The results suggest that post-transcriptional regulation may drive environmental acclimation by regulating hallmark biological processes. To foster discoveries by other research groups interested in the topic, we extended our integrated data to the public in the form of an interactive atlas (https://halodata.systemsbiology.net).

PMID:36912639 | DOI:10.1128/msystems.00816-22

Curr Opin Otolaryngol Head Neck Surg. 2023 Apr 1;31(2):73-77. doi: 10.1097/MOO.0000000000000870. Epub 2022 Dec 29.

ABSTRACT

PURPOSE OF REVIEW: Solid tumors often establish a locally hypercoagulant state that promotes vascular complications, such as venous thromboembolism (VTE). Oral squamous cell carcinoma (OSCC) is associated with a broad range of hemostatic complications. Although VTE rarely occurs in ambulatory patients with OSCC, the coagulation cascade is typically activated by surgical resection and local hemorrhage. We present the recent progress in the understanding of the role and regulation of coagulation in OSCC.

RECENT FINDINGS: Application of systems biology, using bulk tumor and single cell genomic analyses, unveiled the landscape of the tumor coagulome. Of all tumor types, OSCC express the highest mRNA levels of F3 and PLAU, the genes that encode the tissue factor (TF) and urokinase-type plasminogen activator (uPA), the key regulators of coagulation and fibrinolysis, respectively. It also brought to light the intimate and reciprocal regulation between coagulation/fibrinolysis and the tumor microenvironment (TME).

SUMMARY: OSCC have a specific coagulome, with consequences that likely extend beyond the vascular risk. We discuss the attractive possibility that biomarkers of the coagulation cascade might reflect some important characteristics of the TME, offering new opportunities to better understand the impact of surgical procedures, better predict their oncological outcome and improve current therapeutic approaches.

PMID:36912218 | DOI:10.1097/MOO.0000000000000870

Nucleic Acids Res. 2023 Mar 13:gkad157. doi: 10.1093/nar/gkad157. Online ahead of print.

ABSTRACT

Multimodal single-cell omics technologies enable multiple molecular programs to be simultaneously profiled at a global scale in individual cells, creating opportunities to study biological systems at a resolution that was previously inaccessible. However, the analysis of multimodal single-cell omics data is challenging due to the lack of methods that can integrate across multiple data modalities generated from such technologies. Here, we present Matilda, a multi-task learning method for integrative analysis of multimodal single-cell omics data. By leveraging the interrelationship among tasks, Matilda learns to perform data simulation, dimension reduction, cell type classification, and feature selection in a single unified framework. We compare Matilda with other state-of-the-art methods on datasets generated from some of the most popular multimodal single-cell omics technologies. Our results demonstrate the utility of Matilda for addressing multiple key tasks on integrative multimodal single-cell omics data analysis. Matilda is implemented in Pytorch and is freely available from https://github.com/PYangLab/Matilda.

PMID:36912104 | DOI:10.1093/nar/gkad157

Heliyon. 2023 Mar;9(3):e14115. doi: 10.1016/j.heliyon.2023.e14115. Epub 2023 Mar 6.

ABSTRACT

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis.

PMID:36911878 | PMC:PMC9986505 | DOI:10.1016/j.heliyon.2023.e14115

Ecol Evol. 2023 Mar 8;13(3):e9874. doi: 10.1002/ece3.9874. eCollection 2023 Mar.

ABSTRACT

The study of individual fungi and their communities is of great interest to modern biology because they might be both producers of useful compounds, such as antibiotics and organic acids, and pathogens of various diseases. And certain features associated with the functional capabilities of fungi are determined by differences in gene content. Information about gene content is most often taken from the results of functional annotation of the whole genome. However, in practice, whole genome sequencing of fungi is rarely performed. At the same time, usually sequence amplicons of the ITS region to identify fungal taxonomy. But in the case of amplicon sequencing there is no way to perform a functional annotation. Here, we present FunFun, the instrument that allows to evaluate the gene content of an individual fungus or mycobiome from ITS sequencing data. FunFun algorithm based on a modified K-nearest neighbors algorithm. As input, the program can use ITS1, ITS2, or a full-size ITS cluster (ITS1-5.8S-ITS2). FunFun was realized as a pip-installed command line instrument and validated using a shuffle-split approach. The developed instrument can be very useful in the fungal community comparing and estimating functional capabilities of fungi under study. Also, the program can predict with high accuracy the most variable functions.

PMID:36911300 | PMC:PMC9994472 | DOI:10.1002/ece3.9874

Adv Genet (Hoboken). 2022 Nov 9;3(4):2200014. doi: 10.1002/ggn2.202200014. eCollection 2022 Dec.

ABSTRACT

Cancer is the second leading cause of death globally, with therapeutic resistance being a major cause of treatment failure in the clinic. The dynamic signaling that occurs between tumor cells and the diverse cells of the surrounding tumor microenvironment actively promotes disease progression and therapeutic resistance. Improving the understanding of how tumors evolve following therapy and the molecular mechanisms underpinning de novo or acquired resistance is thus critical for the identification of new targets and for the subsequent development of more effective combination regimens. Simultaneously targeting multiple hallmark capabilities of cancer to circumvent adaptive or evasive resistance may lead to significantly improved treatment response in the clinic. Here, the latest applications of functional genomics tools, such as clustered regularly interspaced short palindromic repeats (CRISPR) editing, to characterize the dynamic cancer resistance mechanisms, from improving the understanding of resistance to classical chemotherapeutics, to deciphering unique mechanisms that regulate tumor responses to new targeted agents and immunotherapies, are discussed. Potential avenues of future research in combating therapeutic resistance, the contribution of tumor-stroma signaling in this setting, and how advanced functional genomics tools can help streamline the identification of key molecular determinants of drug response are explored.

PMID:36911295 | PMC:PMC9993475 | DOI:10.1002/ggn2.202200014

ArXiv. 2023 Mar 2:arXiv:2303.00933v1. Preprint.

ABSTRACT

Spatially annotated single-cell datasets provide unprecedented opportunities to dissect cell-cell communication in development and disease. Heterotypic signaling includes interactions between different cell types and is well established in tissue development and spatial organization. Epithelial organization requires several different programs that are tightly regulated. Planar cell polarity is the organization of epithelial cells along the planar axis orthogonal to the apical-basal axis. In this study, we investigate planar cell polarity factors and explore the implications of developmental regulators as malignant drivers. Utilizing cancer systems biology analysis, we derive gene expression network for WNT-ligands (WNT) and their cognate frizzled (FZD) receptors in skin cutaneous melanoma. The profiles supported by unsupervised clustering of multiple-sequence alignments identify ligand-independent signaling and implications for metastatic progression based on the underpinning developmental spatial program. Omics studies and spatial biology connect developmental programs with oncological events and explain key spatial features of metastatic aggressiveness. Dysregulation of prominent planar cell polarity factors such specific representative of the WNT and FZD families in malignant melanoma recapitulates the development program of normal melanocytes but in an uncontrolled and disorganized fashion.

PMID:36911273 | PMC:PMC10002759

Front Psychiatry. 2023 Feb 22;14:1085445. doi: 10.3389/fpsyt.2023.1085445. eCollection 2023.

ABSTRACT

Over the past decade, precision medicine has become one of the most influential approaches in biomedical research to improve early detection, diagnosis, and prognosis of clinical conditions and develop mechanism-based therapies tailored to individual characteristics using biomarkers. This perspective article first reviews the origins and concept of precision medicine approaches to autism and summarises recent findings from the first "generation" of biomarker studies. Multi-disciplinary research initiatives created substantially larger, comprehensively characterised cohorts, shifted the focus from group-comparisons to individual variability and subgroups, increased methodological rigour and advanced analytic innovations. However, although several candidate markers with probabilistic value have been identified, separate efforts to divide autism by molecular, brain structural/functional or cognitive markers have not identified a validated diagnostic subgroup. Conversely, studies of specific monogenic subgroups revealed substantial variability in biology and behaviour. The second part discusses both conceptual and methodological factors in these findings. It is argued that the predominant reductionist approach, which seeks to parse complex issues into simpler, more tractable units, let us to neglect the interactions between brain and body, and divorce individuals from their social environment. The third part draws on insights from systems biology, developmental psychology and neurodiversity approaches to outline an integrative approach that considers the dynamic interaction between biological (brain, body) and social mechanisms (stress, stigma) to understanding the origins of autistic features in particular conditions and contexts. This requires 1) closer collaboration with autistic people to increase face validity of concepts and methodologies; (2) development of measures/technologies that enable repeat assessment of social and biological factors in different (naturalistic) conditions and contexts, (3) new analytic methods to study (simulate) these interactions (including emergent properties), and (4) cross-condition designs to understand which mechanisms are transdiagnostic or specific for particular autistic sub-populations. Tailored support may entail both creating more favourable conditions in the social environment and interventions for some autistic people to increase well-being.

PMID:36911126 | PMC:PMC9992810 | DOI:10.3389/fpsyt.2023.1085445

Adv Genet (Hoboken). 2022 Aug 25;4(1):2200016. doi: 10.1002/ggn2.202200016. eCollection 2023 Mar.

ABSTRACT

The Global Alliance for Genomics and Health (GA4GH) is developing a suite of coordinated standards for genomics for healthcare. The Phenopacket is a new GA4GH standard for sharing disease and phenotype information that characterizes an individual person, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments. A detailed example is presented that illustrates how to use the schema to represent the clinical course of a patient with retinoblastoma, including demographic information, the clinical diagnosis, phenotypic features and clinical measurements, an examination of the extirpated tumor, therapies, and the results of genomic analysis. The Phenopacket Schema, together with other GA4GH data and technical standards, will enable data exchange and provide a foundation for the computational analysis of disease and phenotype information to improve our ability to diagnose and conduct research on all types of disorders, including cancer and rare diseases.

PMID:36910590 | PMC:PMC10000265 | DOI:10.1002/ggn2.202200016

iScience. 2023 Apr 21;26(4):106282. doi: 10.1016/j.isci.2023.106282. Epub 2023 Feb 27.

ABSTRACT

Three-dimensional structure and dynamics are essential for protein function. Advancements in hydrogen-deuterium exchange (HDX) techniques enable probing protein dynamic information in physiologically relevant conditions. HDX-coupled mass spectrometry (HDX-MS) has been broadly applied in pharmaceutical industries. However, it is challenging to obtain dynamics information at the single amino acid resolution and time consuming to perform the experiments and process the data. Here, we demonstrate the first deep learning model, artificial intelligence-based HDX (AI-HDX), that predicts intrinsic protein dynamics based on the protein sequence. It uncovers the protein structural dynamics by combining deep learning, experimental HDX, sequence alignment, and protein structure prediction. AI-HDX can be broadly applied to drug discovery, protein engineering, and biomedical studies. As a demonstration, we elucidated receptor-binding domain structural dynamics as a potential mechanism of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody efficacy and immune escape. AI-HDX fundamentally differs from the current AI tools for protein analysis and may transform protein design for various applications.

PMID:36910327 | PMC:PMC9968663 | DOI:10.1016/j.isci.2023.106282

Front Mol Neurosci. 2023 Feb 22;16:1136398. doi: 10.3389/fnmol.2023.1136398. eCollection 2023.

ABSTRACT

Astrocytes play an important role in the pathogenesis of Alzheimer's disease (AD). It is widely involved in energy metabolism in the brain by providing nutritional and metabolic support to neurons; however, the alteration in the metabolism of astrocytes in AD remains unknown. Through integrative analysis of single-nucleus sequencing datasets, we revealed metabolic changes in various cell types in the prefrontal cortex of patients with AD. We found the depletion of some important metabolites (acetyl-coenzyme A, aspartate, pyruvate, 2-oxoglutarate, glutamine, and others), as well as the inhibition of some metabolic fluxes (glycolysis and tricarbocylic acid cycle, glutamate metabolism) in astrocytes of AD. The abnormality of glutamate metabolism in astrocytes is unique and important. Downregulation of GLUL (GS) and GLUD1 (GDH) may be the cause of glutamate alterations in astrocytes in AD. These results provide a basis for understanding the characteristic changes in astrocytes in AD and provide ideas for the study of AD pathogenesis.

PMID:36910261 | PMC:PMC9992528 | DOI:10.3389/fnmol.2023.1136398

Front Microbiol. 2023 Feb 20;14:1030401. doi: 10.3389/fmicb.2023.1030401. eCollection 2023.

ABSTRACT

INTRODUCTION: Multidrug-resistant (MDR) pathogens are being recognized as a critical threat to human health if they can form biofilm and, in this sense, biofilm-forming MDR-methicillin resistant Staphylococcus aureus (MRSA) and -Pseudomonas aeruginosa strains are a worse concern. Hence, a growing body of documents has introduced antimicrobial peptides (AMPs) as a substitute candidate for conventional antimicrobial agents against drug-resistant and biofilm-associated infections. We evaluated melittin's antibacterial and antibiofilm activity alone and/or in combination with gentamicin, ciprofloxacin, rifampin, and vancomycin on biofilm-forming MDR-P. aeruginosa and MDR-MRSA strains.

METHODS: Antibacterial tests [antibiogram, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC)], anti-biofilm tests [minimum biofilm inhibition concentration (MBIC), and minimum biofilm eradication concentration (MBEC)], as well as synergistic antibiofilm activity of melittin and antibiotics, were performed. Besides, the influence of melittin alone on the biofilm encoding genes and the cytotoxicity and hemolytic effects of melittin were examined.

RESULTS: MIC, MBC, MBIC, and MBEC indices for melittin were in the range of 0.625-5, 1.25-10, 2.5-20, and 10-40 μg/ml, respectively. The findings found that the combination of melittin AMP with antibiotics was synergistic and fractional biofilm inhibitory concentration index (FBICi) for most tested concentrations was <0.5, resulting in a significant reduction in melittin, gentamicin, ciprofloxacin, vancomycin, and rifampin concentrations by 2-256.4, 2-128, 2-16, 4-64 and 4-8 folds, respectively. This phenomenon reduced the toxicity of melittin, whereby its synergist concentration required for biofilm inhibition did not show cytotoxicity and hemolytic activity. Our findings found that melittin decreased the expression of icaA in S. aureus and LasR in P. aeruginosa genes from 0.1 to 4.11 fold for icaA, and 0.11 to 3.7 fold for LasR, respectively.

CONCLUSION: Overall, the results obtained from our study show that melittin alone is effective against the strong biofilm of MDR pathogens and also offers sound synergistic effects with antibiotics without toxicity. Hence, combining melittin and antibiotics can be a potential candidate for further evaluation of in vivo infections by MDR pathogens.

PMID:36910230 | PMC:PMC9994733 | DOI:10.3389/fmicb.2023.1030401

Front Cell Dev Biol. 2023 Feb 22;11:1155993. doi: 10.3389/fcell.2023.1155993. eCollection 2023.

NO ABSTRACT

PMID:36910144 | PMC:PMC9992956 | DOI:10.3389/fcell.2023.1155993

PNAS Nexus. 2023 Feb 10;2(3):pgad044. doi: 10.1093/pnasnexus/pgad044. eCollection 2023 Mar.

ABSTRACT

Dopamine neurotransmission in the striatum is central to many normal and disease functions. Ventral midbrain dopamine neurons exhibit ongoing tonic firing that produces low extrasynaptic levels of dopamine below the detection of conventional extrasynaptic cyclic voltammetry (∼10-20 nanomolar), with superimposed bursts that can saturate the dopamine uptake transporter and produce transient micromolar concentrations. The bursts are known to lead to marked presynaptic plasticity via multiple mechanisms, but analysis methods for these kinetic parameters are limited. To provide a deeper understanding of the mechanics of the modulation of dopamine neurotransmission by physiological, genetic, and pharmacological means, we present three computational models of dopamine release with different levels of spatiotemporal complexity to analyze in vivo fast-scan cyclic voltammetry recordings from the dorsal striatum of mice. The models accurately fit to cyclic voltammetry data and provide estimates of presynaptic dopamine facilitation/depression kinetics and dopamine transporter reuptake kinetics, and we used the models to analyze the role of synuclein proteins in neurotransmission. The models' results support recent findings linking the presynaptic protein α-synuclein to the short-term facilitation and long-term depression of dopamine release, as well as reveal a new role for β-synuclein and/or γ-synuclein in the long-term regulation of dopamine reuptake.

PMID:36909827 | PMC:PMC10003750 | DOI:10.1093/pnasnexus/pgad044

bioRxiv. 2023 Feb 27:2023.02.24.529835. doi: 10.1101/2023.02.24.529835. Preprint.

ABSTRACT

Models that are formulated as ordinary differential equations (ODEs) can accurately explain temporal gene expression patterns and promise to yield new insights into important cellular processes, disease progression, and intervention design. Learning such ODEs is challenging, since we want to predict the evolution of gene expression in a way that accurately encodes the causal gene-regulatory network (GRN) governing the dynamics and the nonlinear functional relationships between genes. Most widely used ODE estimation methods either impose too many parametric restrictions or are not guided by meaningful biological insights, both of which impedes scalability and/or explainability. To overcome these limitations, we developed PHOENIX, a modeling framework based on neural ordinary differential equations (NeuralODEs) and Hill-Langmuir kinetics, that can flexibly incorporate prior domain knowledge and biological constraints to promote sparse, biologically interpretable representations of ODEs. We test accuracy of PHOENIX in a series of in silico experiments benchmarking it against several currently used tools for ODE estimation. We also demonstrate PHOENIX’s flexibility by studying oscillating expression data from synchronized yeast cells and assess its scalability by modelling genome-scale breast cancer expression for samples ordered in pseudotime. Finally, we show how the combination of user-defined prior knowledge and functional forms from systems biology allows PHOENIX to encode key properties of the underlying GRN, and subsequently predict expression patterns in a biologically explainable way. PHOENIX will be available as open source at: https://github.com/QuackenbushLab/phoenix .

PMID:36909563 | PMC:PMC10002636 | DOI:10.1101/2023.02.24.529835

Front Endocrinol (Lausanne). 2023 Feb 23;14:1083048. doi: 10.3389/fendo.2023.1083048. eCollection 2023.

ABSTRACT

Heterogeneity is a complex feature of cells and tissues with many interacting components. Depending on the nature of the research context, interacting features of cellular, drug response, genetic, molecular, spatial, temporal, and vascular heterogeneity may be present. We describe the various forms of heterogeneity with examples of their interactions and how they play a role in affecting cellular phenotype and drug responses in breast cancer. While cellular heterogeneity may be the most widely described and invoked, many forms of heterogeneity are evident within the tumor microenvironment and affect responses to the endocrine and cytotoxic drugs widely used in standard clinical care. Drug response heterogeneity is a critical determinant of clinical response and curative potential and also is multifaceted when encountered. The interactive nature of some forms of heterogeneity is readily apparent. For example, the process of metastasis has the properties of both temporal and spatial heterogeneity within the host, whereas each individual metastatic deposit may exhibit cellular, genetic, molecular, and vascular heterogeneity. This review describes the many forms of heterogeneity, their integrated activities, and offers some insights into how heterogeneity may be understood and studied in the future.

PMID:36909339 | PMC:PMC9997040 | DOI:10.3389/fendo.2023.1083048

Front Public Health. 2023 Feb 24;11:1109158. doi: 10.3389/fpubh.2023.1109158. eCollection 2023.

ABSTRACT

BACKGROUND: Self-management is important for the blood sugar control of middle-aged and elderly Type 2 diabetes mellitus (T2DM) patients, of which diet, exercise, and drug compliance are the most common components. The Information-Motivation-Behavioral Skills Model (IMB) has been widely used in health behavior management and intervention.

OBJECTIVE: The purpose of this study is to develop and validate the Diabetic Self-Management Scale (DSMS) based on the IMB model.

METHODS: Self-report survey data was collected from middle-aged and elderly T2DM patients in Zhongmu City, Henan Province, China in November 2021 using convenience sampling. The original DSMS was developed through a literature review and summary of previous similar scales using an inductive approach. Item modification was finished by a panel of specialists. Exploratory factor analysis and confirmatory factor analysis were used to evaluate the reliability, convergent validity, discriminant validity, and criterion validity of DSMS.

RESULTS: Four hundred and sixty nine T2DM patients completed the questionnaire survey. The final DSMS consists of 22 items with three dimensions, including information (five items), motivation (eight items), and behavior skills (nine items). The results of simple factor analysis showed that the KMO value was 0.839, Bartlett spherical test 2 = 3254.872, P < 0.001. The results of confirmatory factor analysis showed that 2/df = 2.261, RMSEA = 0.073, CFI = 0.937, TLI = 0.930, and SRMR = 0.096. The standardized factor loadings of 22 DSMS items were all above 0.6, and the CR values of 3 dimensions were all higher than 0.9. In addition, DSMS also showed good discriminant and criterion validity.

CONCLUSION: The 22-item DSMS has good reliability and validity, and can be used to make diabetic self-management assessment regarding diet, physical activity, and medication among middle-aged and elderly Chinese T2DM patients. DSMS is of moderate length and easy to understand. It can be promoted in China in the future to understand the self-management status of middle-aged and elderly T2DM patients in China.

PMID:36908406 | PMC:PMC9998917 | DOI:10.3389/fpubh.2023.1109158