Tanaffos. 2022 Mar;21(3):336-347.

ABSTRACT

BACKGROUND: Chronic obstructive pulmonary disease (COPD) as one of the health-threatening problems imposes many economic costs on health systems. Today, there is a greater tendency to use complementary and alternative therapies in the treatment of diseases. This study aimed to evaluate the efficacy of a Persian herbal formulation in patients with COPD.

MATERIALS AND METHODS: This randomized clinical trial was conducted on 76 patients with mild-severe COPD assigned to 2 groups (in each group n=38) for 8 weeks. The interventional group received Compound Honey Syrup (CHS), consisting of combination of honey and extracts of five medicinal plants (i.e., ginger, cinnamon, saffron, cardamom, and galangal) and the control group received a placebo. The COPD Assessment Test (CAT), St George's Respiratory Questionnaire (SGRQ), and lung function test were used before and after.

RESULTS: Seventy-six patients, 88.6% male and 55.7% under 60 years of age, completed the course of treatment. At the end of the study, the overall score of the CAT questionnaire was significantly different between the first and fourth week (P=0.029). Meanwhile the findings of SGRQ questionnaire were significantly different between the interventional and control groups at other times (P=0.001). FEV1 and FEV1/FVC were found to be significantly different between two groups in weeks 4 and 8 (P <0.05). At the end of the study, no side effects of CHS were reported.

CONCLUSION: Based on the data presented herein, CHS could be effective as a complementary and safe drug in increasing the quality of life of with COPD.

PMID:37025308 | PMC:PMC10073945

Front Cell Dev Biol. 2023 Mar 21;11:1124874. doi: 10.3389/fcell.2023.1124874. eCollection 2023.

ABSTRACT

All cells employ signal transduction pathways to respond to physiologically relevant extracellular cytokines, stressors, nutrient levels, hormones, morphogens, and other stimuli that vary in concentration and rate in healthy and diseased states. A central unsolved fundamental question in cell signaling is whether and how cells sense and integrate information conveyed by changes in the rate of extracellular stimuli concentrations, in addition to the absolute difference in concentration. We propose that different environmental changes over time influence cell behavior in addition to different signaling molecules or different genetic backgrounds. However, most current biomedical research focuses on acute environmental changes and does not consider how cells respond to environments that change slowly over time. As an example of such environmental change, we review cell sensitivity to environmental rate changes, including the novel mechanism of rate threshold. A rate threshold is defined as a threshold in the rate of change in the environment in which a rate value below the threshold does not activate signaling and a rate value above the threshold leads to signal activation. We reviewed p38/Hog1 osmotic stress signaling in yeast, chemotaxis and stress response in bacteria, cyclic adenosine monophosphate signaling in Amoebae, growth factors signaling in mammalian cells, morphogen dynamics during development, temporal dynamics of glucose and insulin signaling, and spatio-temproral stressors in the kidney. These reviewed examples from the literature indicate that rate thresholds are widespread and an underappreciated fundamental property of cell signaling. Finally, by studying cells in non-linear environments, we outline future directions to understand cell physiology better in normal and pathophysiological conditions.

PMID:37025183 | PMC:PMC10072286 | DOI:10.3389/fcell.2023.1124874

Front Plant Sci. 2023 Mar 21;14:1139106. doi: 10.3389/fpls.2023.1139106. eCollection 2023.

ABSTRACT

Holliday junction (HJ) is a four-way structured DNA intermediate in homologous recombination. In bacteria, the HJ-specific binding protein RuvA and the motor protein RuvB together form the RuvAB complex to catalyze HJ branch migration. Pseudomonas aeruginosa (P. aeruginosa, Pa) is a ubiquitous opportunistic bacterial pathogen that can cause serious infection in a variety of host species, including vertebrate animals, insects and plants. Here, we describe the cryo-Electron Microscopy (cryo-EM) structure of the RuvAB-HJ intermediate complex from P. aeruginosa. The structure shows that two RuvA tetramers sandwich HJ at the junction center and disrupt base pairs at the branch points of RuvB-free HJ arms. Eight RuvB subunits are recruited by the RuvA octameric core and form two open-rings to encircle two opposite HJ arms. Each RuvB subunit individually binds a RuvA domain III. The four RuvB subunits within the ring display distinct subdomain conformations, and two of them engage the central DNA duplex at both strands with their C-terminal β-hairpins. Together with the biochemical analyses, our structure implicates a potential mechanism of RuvB motor assembly onto HJ DNA.

PMID:37025142 | PMC:PMC10071043 | DOI:10.3389/fpls.2023.1139106

Front Plant Sci. 2023 Mar 21;14:1158933. doi: 10.3389/fpls.2023.1158933. eCollection 2023.

ABSTRACT

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.

PMID:37025141 | PMC:PMC10070872 | DOI:10.3389/fpls.2023.1158933

Front Genet. 2023 Apr 6;14:1029758. doi: 10.3389/fgene.2023.1029758. eCollection 2023.

ABSTRACT

Single-cell sequencing has become one of the most used techniques across the wide field of biology. It has enabled researchers to investigate the whole transcriptome at the cellular level across tissues, which unlocks numerous potentials for basic and applied studies in future diagnosis and therapy. Here, we review the impact of single-cell RNA sequencing, as the prominent single-cell technique, in pancreatic biology and cancer. We discuss the most recent findings about pancreatic physiology and pathophysiology owing to this technological advancement in the past few years. Using single-cell RNA sequencing, researchers have been able to discover cellular heterogeneity across healthy cell types, as well as cancer tissues of the pancreas. We will discuss the new immunological targets and new molecular mechanisms of progression in the microenvironment of pancreatic cancer studied using single-cell RNA sequencing. The scope is not limited to cancer tissues, and we cover novel developmental, evolutionary, physiological, and heterogenic insights that have also been achieved recently for pancreatic tissues. We cover all biological insights derived from the single-cell RNA sequencing data, discuss the corresponding pros and cons, and finally, conclude how future research can move better by utilizing single-cell analysis for pancreatic biology.

PMID:37091793 | PMC:PMC10115972 | DOI:10.3389/fgene.2023.1029758

Nat Cell Biol. 2023 Apr 6. doi: 10.1038/s41556-023-01114-y. Online ahead of print.

ABSTRACT

DNA and Histone 3 Lysine 27 methylation typically function as repressive modifications and operate within distinct genomic compartments. In mammals, the majority of the genome is kept in a DNA methylated state, whereas the Polycomb repressive complexes regulate the unmethylated CpG-rich promoters of developmental genes. In contrast to this general framework, the extra-embryonic lineages display non-canonical, globally intermediate DNA methylation levels, including disruption of local Polycomb domains. Here, to better understand this unusual landscape's molecular properties, we genetically and chemically perturbed major epigenetic pathways in mouse trophoblast stem cells. We find that the extra-embryonic epigenome reflects ongoing and dynamic de novo methyltransferase recruitment, which is continuously antagonized by Polycomb to maintain intermediate, locally disordered methylation. Despite its disorganized molecular appearance, our data point to a highly controlled equilibrium between counteracting repressors within extra-embryonic cells, one that can seemingly persist indefinitely without bistable features typically seen for embryonic forms of epigenetic regulation.

PMID:37024684 | DOI:10.1038/s41556-023-01114-y

Nat Methods. 2023 Apr 6. doi: 10.1038/s41592-023-01831-0. Online ahead of print.

ABSTRACT

The discovery of biomolecular condensates transformed our understanding of intracellular compartmentalization of molecules. To integrate interdisciplinary scientific knowledge about the function and composition of biomolecular condensates, we developed the crowdsourcing condensate database and encyclopedia ( cd-code.org ). CD-CODE is a community-editable platform, which includes a database of biomolecular condensates based on the literature, an encyclopedia of relevant scientific terms and a crowdsourcing web application. Our platform will accelerate the discovery and validation of biomolecular condensates, and facilitate efforts to understand their role in disease and as therapeutic targets.

PMID:37024650 | DOI:10.1038/s41592-023-01831-0

Nat Methods. 2023 Apr 6. doi: 10.1038/s41592-023-01832-z. Online ahead of print.

ABSTRACT

Major computational challenges exist in relation to the collection, curation, processing and analysis of large genomic and imaging datasets, as well as the simulation of larger and more realistic models in systems biology. Here we discuss how a relative newcomer among programming languages-Julia-is poised to meet the current and emerging demands in the computational biosciences and beyond. Speed, flexibility, a thriving package ecosystem and readability are major factors that make high-performance computing and data analysis available to an unprecedented degree. We highlight how Julia's design is already enabling new ways of analyzing biological data and systems, and we provide a list of resources that can facilitate the transition into Julian computing.

PMID:37024649 | DOI:10.1038/s41592-023-01832-z

Nat Genet. 2023 Apr 6. doi: 10.1038/s41588-023-01366-2. Online ahead of print.

ABSTRACT

Transcriptional regulation exhibits extensive robustness, but human genetics indicates sensitivity to transcription factor (TF) dosage. Reconciling such observations requires quantitative studies of TF dosage effects at trait-relevant ranges, largely lacking so far. TFs play central roles in both normal-range and disease-associated variation in craniofacial morphology; we therefore developed an approach to precisely modulate TF levels in human facial progenitor cells and applied it to SOX9, a TF associated with craniofacial variation and disease (Pierre Robin sequence (PRS)). Most SOX9-dependent regulatory elements (REs) are buffered against small decreases in SOX9 dosage, but REs directly and primarily regulated by SOX9 show heightened sensitivity to SOX9 dosage; these RE responses partially predict gene expression responses. Sensitive REs and genes preferentially affect functional chondrogenesis and PRS-like craniofacial shape variation. We propose that such REs and genes underlie the sensitivity of specific phenotypes to TF dosage, while buffering of other genes leads to robust, nonlinear dosage-to-phenotype relationships.

PMID:37024583 | DOI:10.1038/s41588-023-01366-2

Nat Genet. 2023 Apr 6. doi: 10.1038/s41588-023-01355-5. Online ahead of print.

ABSTRACT

Anti-PD-1/PD-L1 agents have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). To expand our understanding of the molecular features underlying response to checkpoint inhibitors in NSCLC, we describe here the first joint analysis of the Stand Up To Cancer-Mark Foundation cohort, a resource of whole exome and/or RNA sequencing from 393 patients with NSCLC treated with anti-PD-(L)1 therapy, along with matched clinical response annotation. We identify a number of associations between molecular features and outcome, including (1) favorable (for example, ATM altered) and unfavorable (for example, TERT amplified) genomic subgroups, (2) a prominent association between expression of inducible components of the immunoproteasome and response and (3) a dedifferentiated tumor-intrinsic subtype with enhanced response to checkpoint blockade. Taken together, results from this cohort demonstrate the complexity of biological determinants underlying immunotherapy outcomes and reinforce the discovery potential of integrative analysis within large, well-curated, cancer-specific cohorts.

PMID:37024582 | DOI:10.1038/s41588-023-01355-5

Nestle Nutr Inst Workshop Ser. 2023;97:30-40. doi: 10.1159/000528992. Epub 2023 Apr 6.

ABSTRACT

Human milk contains all nutritive and bioactive compounds to give infants the best possible start in life. Human milk bioactives cover a broad range of components, including immune cells, antimicrobial proteins, microbes, and human milk oligosaccharides (HMOs). Over the last decade, HMOs have gained special attention as their industrial production has allowed the study of their structure-function relation in reductionist experimental setups. This has shed light on how HMOs steer microbiome and immune system development in early life but also how HMOs affect infant health (e.g., antibiotic use, respiratory tract infections). We are on the verge of a new era where we can examine human milk as a complex biological system. This allows not only study of the mode of action and causality of individual human milk components but also investigation of synergistic effects that might exist between different bioactives. This new wave in human milk research is largely fueled by significant advances in analytical tools in the field of systems biology and network analysis. It will be exciting to explore how human milk composition is affected by different factors, how different human milk compounds work together, and how this influences healthy infant development.

PMID:37023733 | DOI:10.1159/000528992

J Chem Inf Model. 2023 Apr 6. doi: 10.1021/acs.jcim.2c01291. Online ahead of print.

ABSTRACT

Drug repurposing or repositioning (DR) refers to finding new therapeutic applications for existing drugs. Current computational DR methods face data representation and negative data sampling challenges. Although retrospective studies attempt to operate various representations, it is a crucial step for an accurate prediction to aggregate these features and bring the associations between drugs and diseases into a unified latent space. In addition, the number of unknown associations between drugs and diseases, which is considered negative data, is much higher than the number of known associations, or positive data, leading to an imbalanced dataset. In this regard, we propose the DrugRep-KG method, which applies a knowledge graph embedding approach for representing drugs and diseases, to address these challenges. Despite the typical DR methods that consider all unknown drug-disease associations as negative data, we select a subset of unknown associations, provided the disease occurs because of an adverse reaction to a drug. DrugRep-KG has been evaluated based on different settings and achieves an AUC-ROC (area under the receiver operating characteristic curve) of 90.83% and an AUC-PR (area under the precision-recall curve) of 90.10%, which are higher than in previous works. Besides, we checked the performance of our framework in finding potential drugs for coronavirus infection and skin-related diseases: contact dermatitis and atopic eczema. DrugRep-KG predicted beclomethasone for contact dermatitis, and fluorometholone, clocortolone, fluocinonide, and beclomethasone for atopic eczema, all of which have previously been proven to be effective in other studies. Fluorometholone for contact dermatitis is a novel suggestion by DrugRep-KG that should be validated experimentally. DrugRep-KG also predicted the associations between COVID-19 and potential treatments suggested by DrugBank, in addition to new drug candidates provided with experimental evidence. The data and code underlying this article are available at https://github.com/CBRC-lab/DrugRep-KG.

PMID:37023229 | DOI:10.1021/acs.jcim.2c01291

Science. 2023 Apr 7;380(6640):eadc9498. doi: 10.1126/science.adc9498. Epub 2023 Apr 7.

ABSTRACT

Despite the vast diversity of the antibody repertoire, infected individuals often mount antibody responses to precisely the same epitopes within antigens. The immunological mechanisms underpinning this phenomenon remain unknown. By mapping 376 immunodominant "public epitopes" at high resolution and characterizing several of their cognate antibodies, we concluded that germline-encoded sequences in antibodies drive recurrent recognition. Systematic analysis of antibody-antigen structures uncovered 18 human and 21 partially overlapping mouse germline-encoded amino acid-binding (GRAB) motifs within heavy and light V gene segments that in case studies proved critical for public epitope recognition. GRAB motifs represent a fundamental component of the immune system's architecture that promotes recognition of pathogens and leads to species-specific public antibody responses that can exert selective pressure on pathogens.

PMID:37023193 | DOI:10.1126/science.adc9498

Science. 2023 Apr 7;380(6640):30-32. doi: 10.1126/science.adh8182. Epub 2023 Apr 6.

NO ABSTRACT

PMID:37023192 | DOI:10.1126/science.adh8182

Proc Natl Acad Sci U S A. 2023 Apr 11;120(15):e2216698120. doi: 10.1073/pnas.2216698120. Epub 2023 Apr 6.

ABSTRACT

Discovering DNA regulatory sequence motifs and their relative positions is vital to understanding the mechanisms of gene expression regulation. Although deep convolutional neural networks (CNNs) have achieved great success in predicting cis-regulatory elements, the discovery of motifs and their combinatorial patterns from these CNN models has remained difficult. We show that the main difficulty is due to the problem of multifaceted neurons which respond to multiple types of sequence patterns. Since existing interpretation methods were mainly designed to visualize the class of sequences that can activate the neuron, the resulting visualization will correspond to a mixture of patterns. Such a mixture is usually difficult to interpret without resolving the mixed patterns. We propose the NeuronMotif algorithm to interpret such neurons. Given any convolutional neuron (CN) in the network, NeuronMotif first generates a large sample of sequences capable of activating the CN, which typically consists of a mixture of patterns. Then, the sequences are "demixed" in a layer-wise manner by backward clustering of the feature maps of the involved convolutional layers. NeuronMotif can output the sequence motifs, and the syntax rules governing their combinations are depicted by position weight matrices organized in tree structures. Compared to existing methods, the motifs found by NeuronMotif have more matches to known motifs in the JASPAR database. The higher-order patterns uncovered for deep CNs are supported by the literature and ATAC-seq footprinting. Overall, NeuronMotif enables the deciphering of cis-regulatory codes from deep CNs and enhances the utility of CNN in genome interpretation.

PMID:37023129 | DOI:10.1073/pnas.2216698120

PLoS One. 2023 Apr 6;18(4):e0282122. doi: 10.1371/journal.pone.0282122. eCollection 2023.

ABSTRACT

The capacity of cells to process information is currently used to design cell-based tools for ecological, industrial, and biomedical applications such as detecting dangerous chemicals or for bioremediation. In most applications, individual cells are used as the information processing unit. However, single cell engineering is limited by the necessary molecular complexity and the accompanying metabolic burden of synthetic circuits. To overcome these limitations, synthetic biologists have begun engineering multicellular systems that combine cells with designed subfunctions. To further advance information processing in synthetic multicellular systems, we introduce the application of reservoir computing. Reservoir computers (RCs) approximate a temporal signal processing task via a fixed-rule dynamic network (the reservoir) with a regression-based readout. Importantly, RCs eliminate the need of network rewiring, as different tasks can be approximated with the same reservoir. Previous work has already demonstrated the capacity of single cells, as well as populations of neurons, to act as reservoirs. In this work, we extend reservoir computing in multicellular populations with the widespread mechanism of diffusion-based cell-to-cell signaling. As a proof-of-concept, we simulated a reservoir made of a 3D community of cells communicating via diffusible molecules and used it to approximate a range of binary signal processing tasks, focusing on two benchmark functions-computing median and parity functions from binary input signals. We demonstrate that a diffusion-based multicellular reservoir is a feasible synthetic framework for performing complex temporal computing tasks that provides a computational advantage over single cell reservoirs. We also identified a number of biological properties that can affect the computational performance of these processing systems.

PMID:37023084 | DOI:10.1371/journal.pone.0282122

J Mol Evol. 2023 Apr 6. doi: 10.1007/s00239-023-10101-8. Online ahead of print.

ABSTRACT

TenA thiamin-degrading enzymes are commonly found in prokaryotes, plants, fungi and algae and are involved in the thiamin salvage pathway. The gut symbiont Bacteroides thetaiotaomicron (Bt) produces a TenA protein (BtTenA) which is packaged into its extracellular vesicles. An alignment of BtTenA protein sequence with proteins from different databases using the basic local alignment search tool (BLAST) and the generation of a phylogenetic tree revealed that BtTenA is related to TenA-like proteins not only found in a small number of intestinal bacterial species but also in some aquatic bacteria, aquatic invertebrates, and freshwater fish. This is, to our knowledge, the first report describing the presence of TenA-encoding genes in the genome of members of the animal kingdom. By searching metagenomic databases of diverse host-associated microbial communities, we found that BtTenA homologues were mostly represented in biofilms present on the surface of macroalgae found in Australian coral reefs. We also confirmed the ability of a recombinant BtTenA to degrade thiamin. Our study shows that BttenA-like genes which encode a novel sub-class of TenA proteins are sparingly distributed across two kingdoms of life, a feature of accessory genes known for their ability to spread between species through horizontal gene transfer.

PMID:37022443 | DOI:10.1007/s00239-023-10101-8

Leuk Lymphoma. 2023 Apr 6:1-10. doi: 10.1080/10428194.2023.2197538. Online ahead of print.

ABSTRACT

Philadelphia (Ph)-like acute lymphoblastic leukemia (ALL) is a high-risk subgroup of B cell ALL with distinct genotypes, unified by gene expression profile similar to Ph-positive ALL, but lacking the BCR::ABL1 fusion. Ph-like ALL patients respond inadequately to conventional chemotherapy with higher rates of induction failure, persistent measurable residual disease, and lower survival rates compared to other B cell ALL subtypes. Considering Ph-like ALL's chemo-refractory nature, there is an interest in pursuing innovative therapeutic approaches to treat, including the combination of tyrosine kinase inhibitors with frontline regimens, and early introduction of novel antibody-drug conjugates and immunotherapies. Accurate diagnosis and disease-risk stratification are key to increase access for high-risk patients to allogeneic hematopoietic cell transplantation in their first complete remission. In this review, we will discuss our current knowledge of pathogenesis of Ph-like ALL, diagnostic strategies, as well as emerging data on new and current treatment strategies for this disease.

PMID:37021793 | DOI:10.1080/10428194.2023.2197538

Sleep. 2023 Apr 6:zsad101. doi: 10.1093/sleep/zsad101. Online ahead of print.

ABSTRACT

Research into sleep-wake behaviours relies on scoring sleep states, normally done by manual inspection of electroencephalogram (EEG) and electromyogram (EMG) recordings. This is a highly time-consuming process prone to inter-rater variability. When studying relationships between sleep and motor function, analyzing arousal states under a four-state system of active wake (AW), quiet wake (QW), non-rapid-eye-movement (NREM) sleep, and rapid-eye-movement (REM) sleep provides greater precision in behavioural analysis but is a more complex model for classification than the traditional three-state identification (wake, NREM, and REM sleep) usually used in rodent models. Characteristic features between sleep-wake states provide potential for the use of machine learning to automate classification. Here, we devised SleepEns, which uses a novel ensemble architecture, the time-series ensemble. SleepEns achieved 90% accuracy to the source expert, which was statistically similar to the performance of two other human experts. Considering the capacity for classification disagreements that are still physiologically reasonable, SleepEns had an acceptable performance of 99% accuracy, as determined blindly by the source expert. Classifications given by SleepEns also maintained similar sleep-wake characteristics compared to expert classifications, some of which were essential for sleep-wake identification. Hence, our approach achieves results comparable to human ability in a fraction of the time. This new machine-learning ensemble will significantly impact the ability of sleep researcher to detect and study sleep-wake behaviours in mice and potentially in humans.

PMID:37021715 | DOI:10.1093/sleep/zsad101

iScience. 2023 Mar 21;26(4):106460. doi: 10.1016/j.isci.2023.106460. eCollection 2023 Apr 21.

ABSTRACT

The abundance of biomedical knowledge gained from biological experiments and clinical practices is an invaluable resource for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In this study, we created a comprehensive BKG called the integrative Biomedical Knowledge Hub (iBKH) by harmonizing and integrating information from diverse biomedical resources. To make iBKH easily accessible for biomedical research, we developed a web-based, user-friendly graphical portal that allows fast and interactive knowledge retrieval. Additionally, we also implemented an efficient and scalable graph learning pipeline for discovering novel biomedical knowledge in iBKH. As a proof of concept, we performed our iBKH-based method for computational in-silico drug repurposing for Alzheimer's disease. The iBKH is publicly available.

PMID:37020958 | PMC:PMC10068563 | DOI:10.1016/j.isci.2023.106460