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

Front Public Health. 2023 Mar 20;11:1109426. doi: 10.3389/fpubh.2023.1109426. eCollection 2023.

ABSTRACT

INTRODUCTION: Traumatic brain injury (TBI) is a very important reason for consultation in emergency departments.

METHODS: A hospital cohort study with patients who attended a hospital emergency department between June 1, 2018 and December 31, 2020 due to TBI was studied. Clinical and sociodemographic variables were recorded. The levels of biomarkers and management variables were used. Qualitative variables were analyzed using Pearson's chi-square test, and quantitative variables using the Mann-Whitney U-test. Survival analyses were performed by fitting a multivariable Cox regression model for patient survival during the follow-up of the study in relation to the patient's characteristics upon admission to the emergency department.

RESULTS: A total of 540 patients were included. The mean age was 83 years, and 53.9% of the patients were men. Overall, 112 patients (20.7%) died during the study follow-up. The mortality rate per 100 person-years was 14.33 (11.8-17.24), the most frequent mechanism being falls in the home, with none caused on public roads. The multivariable Cox proportional hazards model showed that survival after TBI was significantly associated with age, S100 levels, Charlson index, patient's institutionalized status, the place where the TBI occurred, and hemoglobin and platelet levels.

DISCUSSION: The most common profile for a patient with a TBI was male and aged between 80 and 90 years. The combination of the variables age, Charlson index, place of TBI occurrence, and hemoglobin and platelet levels could offer early prediction of survival in our population independently of TBI severity. With the data obtained, a therapeutic algorithm could be established for patients suffering from mild TBI, allowing the patient to be supervised at home, avoiding futile referrals to emergency services.

PMID:37020814 | PMC:PMC10067594 | DOI:10.3389/fpubh.2023.1109426

Front Immunol. 2023 Mar 20;14:1072732. doi: 10.3389/fimmu.2023.1072732. eCollection 2023.

ABSTRACT

Sepsis is a complex heterogeneous condition, and the current lack of effective risk and outcome predictors hinders the improvement of its management. Using a reductionist approach leveraging publicly available transcriptomic data, we describe a knowledge gap for the role of ACVR1B (activin A receptor type 1B) in sepsis. ACVR1B, a member of the transforming growth factor-beta (TGF-beta) superfamily, was selected based on the following: 1) induction upon in vitro exposure of neutrophils from healthy subjects with the serum of septic patients (GSE49755), and 2) absence or minimal overlap between ACVR1B, sepsis, inflammation, or neutrophil in published literature. Moreover, ACVR1B expression is upregulated in septic melioidosis, a widespread cause of fatal sepsis in the tropics. Key biological concepts extracted from a series of PubMed queries established indirect links between ACVR1B and "cancer", "TGF-beta superfamily", "cell proliferation", "inhibitors of activin", and "apoptosis". We confirmed our observations by measuring ACVR1B transcript abundance in buffy coat samples obtained from healthy individuals (n=3) exposed to septic plasma (n = 26 melioidosis sepsis cases)ex vivo. Based on our re-investigation of publicly available transcriptomic data and newly generated ex vivo data, we provide perspective on the role of ACVR1B during sepsis. Additional experiments for addressing this knowledge gap are discussed.

PMID:37020544 | PMC:PMC10067751 | DOI:10.3389/fimmu.2023.1072732

Front Bioeng Biotechnol. 2023 Mar 20;11:1165782. doi: 10.3389/fbioe.2023.1165782. eCollection 2023.

NO ABSTRACT

PMID:37020513 | PMC:PMC10067890 | DOI:10.3389/fbioe.2023.1165782

Bioinform Biol Insights. 2023 Mar 30;17:11779322231161918. doi: 10.1177/11779322231161918. eCollection 2023.

ABSTRACT

MicroRNAs (miRNAs) are small noncoding RNAs that act as negative regulators of gene expression at the post-transcriptional level, promoting mRNA degradation or translation repression. Despite the well-described presence of miRNAs in various human tissues, there is still a lack of information about the relationship between miRNAs and the translation regulation in human embryonic stem cells (hESCs) during cardiomyogenesis. Here, we investigate RNA-seq data from hESCs, focusing on distinct stages of cardiomyogenesis and searching for polysome-bound miRNAs that could be involved in translational regulation. We identify miR-6087 as a differentially expressed miRNA at latest steps of cardiomyocyte differentiation. We analyzed the coexpression pattern between the differentially expressed mRNAs and miR-6087, evaluating whether they are predicted targets of the miRNA. We arranged the genes into an interaction network and identified BLM, RFC4, RFC3, and CCNA2 as key genes of the network. A post hoc analysis of the key genes suggests that miR-6087 could act as a regulator of the cell cycle in hESC during cardiomyogenesis.

PMID:37020502 | PMC:PMC10069004 | DOI:10.1177/11779322231161918

Front Bioinform. 2023 Apr 5;3:1150099. doi: 10.3389/fbinf.2023.1150099. eCollection 2023.

ABSTRACT

The transfer of cell type labels from pre-annotated (reference) to newly collected data is an important task in single-cell data analysis. As the number of publicly available annotated datasets which can be used as reference, as well as the number of computational methods for cell type label transfer are constantly growing, rationals to understand and decide which reference design and which method to use for a particular query dataset are needed. Using detailed data visualisations and interpretable statistical assessments, we benchmark a set of popular cell type annotation methods, test their performance on different cell types and study the effects of the design of reference data (e.g., cell sampling criteria, inclusion of multiple datasets in one reference, gene set selection) on the reliability of predictions. Our results highlight the need for further improvements in label transfer methods, as well as preparation of high-quality pre-annotated reference data of adequate sampling from all cell types of interest, for more reliable annotation of new datasets.

PMID:37091908 | PMC:PMC10114588 | DOI:10.3389/fbinf.2023.1150099

Front Plant Sci. 2023 Apr 5;14:1158068. doi: 10.3389/fpls.2023.1158068. eCollection 2023.

ABSTRACT

Chitin soil amendment is known to improve soil quality, plant growth and stress resilience, but the underlying mechanisms are not well understood. In this study, we monitored chitin's effect on lettuce physiology every two weeks through an eight-week growth period, analyzed the early transcriptional reprogramming and related metabolomic changes of lettuce, in response to crab chitin treatment in peat-based potting soil. In commercial growth conditions, chitin amendment still promoted lettuce growth, increased chlorophyll content, the number of leaves and crop head weight from week six. The flavonoid content in lettuce leaves was altered as well, showing an increase at week two but a decrease from week six. Transcriptomic analysis showed that over 300 genes in lettuce root were significantly differentially expressed after chitin soil treatment. Gene Ontology-term (GO) enrichment analysis revealed statistical overrepresentation of GO terms linked to photosynthesis, pigment metabolic process and phenylpropanoid metabolic process. Further analysis of the differentially expressed genes (DEGs) showed that the flavonoid pathway was mostly upregulated whereas the bifurcation of upstream phenylpropanoid pathway towards lignin biosynthesis was mostly downregulated. Metabolomic analysis revealed the upregulation of salicylic acid, chlorogenic acid, ferulic acid, and p-coumaric acid in chitin-treated lettuce seedlings. These phenolic compounds (PCs) mainly influence the phenylpropanoid biosynthesis pathway and may play important roles in plant defense reactions. Our results suggest that chitin soil amendments might activate induced resistance by priming lettuce plants and promote lettuce growth via transcriptional changes.

PMID:37089656 | PMC:PMC10115174 | DOI:10.3389/fpls.2023.1158068

Front Plant Sci. 2023 Apr 5;14:1130665. doi: 10.3389/fpls.2023.1130665. eCollection 2023.

ABSTRACT

This review is a compilation of proteomic studies on forest tree species published in the last decade (2012-2022), mostly focused on the most investigated species, including Eucalyptus, Pinus, and Quercus. Improvements in equipment, platforms, and methods in addition to the increasing availability of genomic data have favored the biological knowledge of these species at the molecular, organismal, and community levels. Integration of proteomics with physiological, biochemical and other large-scale omics in the direction of the Systems Biology, will provide a comprehensive understanding of different biological processes, from growth and development to responses to biotic and abiotic stresses. As main issue we envisage that proteomics in long-living plants will thrive light on the plant responses and resilience to global climate change, contributing to climate mitigation strategies and molecular breeding programs. Proteomics not only will provide a molecular knowledge of the mechanisms of resilience to either biotic or abiotic stresses, but also will allow the identification on key gene products and its interaction. Proteomics research has also a translational character being applied to the characterization of the variability and biodiversity, as well as to wood and non-wood derived products, traceability, allergen and bioactive peptides identification, among others. Even thought, the full potential of proteomics is far from being fully exploited in forest tree research, with PTMs and interactomics being reserved to plant model systems. The most outstanding achievements in forest tree proteomics in the last decade as well as prospects are discussed.

PMID:37089649 | PMC:PMC10114611 | DOI:10.3389/fpls.2023.1130665

Heliyon. 2023 Apr 5;9(4):e15282. doi: 10.1016/j.heliyon.2023.e15282. eCollection 2023 Apr.

ABSTRACT

Integrin Adhesion Complexes (IACs) serve as links between the cytoskeleton and extracellular environment, acting as mechanosensing and signaling hubs. As such, IACs participate in many aspects of cellular motility, tissue morphogenesis, anchorage-dependent growth and cell survival. Focal Adhesion Kinase (FAK) has emerged as a critical organizer of IAC signaling events due to its early recruitment and diverse substrates, and thus has become a genetic and therapeutic target. Here we present the design and characterization of simple, reversible, and scalable Bimolecular Complementation sensors to monitor FAK phosphorylation in living cells. These probes provide novel means to quantify IAC signaling, expanding on the currently available toolkit for interrogating FAK phosphorylation during diverse cellular processes.

PMID:37089315 | PMC:PMC10119766 | DOI:10.1016/j.heliyon.2023.e15282

Cell Genom. 2023 Apr 5;3(4):100295. doi: 10.1016/j.xgen.2023.100295. eCollection 2023 Apr 12.

ABSTRACT

Sea urchins are emblematic models in developmental biology and display several characteristics that set them apart from other deuterostomes. To uncover the genomic cues that may underlie these specificities, we generated a chromosome-scale genome assembly for the sea urchin Paracentrotus lividus and an extensive gene expression and epigenetic profiles of its embryonic development. We found that, unlike vertebrates, sea urchins retained ancestral chromosomal linkages but underwent very fast intrachromosomal gene order mixing. We identified a burst of gene duplication in the echinoid lineage and showed that some of these expanded genes have been recruited in novel structures (water vascular system, Aristotle's lantern, and skeletogenic micromere lineage). Finally, we identified gene-regulatory modules conserved between sea urchins and chordates. Our results suggest that gene-regulatory networks controlling development can be conserved despite extensive gene order rearrangement.

PMID:37082140 | PMC:PMC10112332 | DOI:10.1016/j.xgen.2023.100295

Mov Ecol. 2023 Apr 5;11(1):19. doi: 10.1186/s40462-023-00381-6.

ABSTRACT

BACKGROUND: To understand the ecology of long-distance migrant bird species, it is necessary to study their full annual cycle, including migratory routes and stopovers. This is especially important for species in high-elevation habitats that are particularly vulnerable to environmental change. Here, we investigated both local and global movements during all parts of the annual cycle in a small trans-Saharan migratory bird breeding at high elevation.

METHODS: Recently, multi-sensor geolocators have opened new research opportunities in small-sized migratory organisms. We tagged Northern Wheatears Oenanthe oenanthe from the central-European Alpine population with loggers recording atmospheric pressure and light intensity. We modelled migration routes and identified stopover and non-breeding sites by correlating the atmospheric pressure measured on the birds with global atmospheric pressure data. Furthermore, we compared barrier-crossing flights with other migratory flights and studied the movement behaviour throughout the annual cycle.

RESULTS: All eight tracked individuals crossed the Mediterranean Sea, using islands for short stops, and made longer stopovers in the Atlas highlands. Single non-breeding sites were used during the entire boreal winter and were all located in the same region of the Sahel. Spring migration was recorded for four individuals with similar or slightly different routes compared to autumn. Migratory flights were typically nocturnal and characterized by fluctuating altitudes, frequently reaching 2000 to 4000 m a.s.l, with a maximum of up to 5150 m. Barrier-crossing flights, i.e., over the sea and the Sahara, were longer, higher, and faster compared to flights above favourable stopover habitat. In addition, we detected two types of altitudinal movements at the breeding site. Unexpected regular diel uphill movements were undertaken from the breeding territories towards nearby roosting sites at cliffs, while regional scale movements took place in response to local meteorological conditions during the pre-breeding period.

CONCLUSION: Our data inform on both local and global scale movements, providing new insights into migratory behaviour and local movements in small songbirds. This calls for a wider use of multi-sensor loggers in songbird migration research, especially for investigating both local and global movements in the same individuals.

PMID:37020307 | DOI:10.1186/s40462-023-00381-6