Clin Nutr. 2024 May 9;43(6):1532-1543. doi: 10.1016/j.clnu.2024.05.017. Online ahead of print.

ABSTRACT

BACKGROUND & AIMS: Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disorder, characterized by the accumulation of excess fat in the liver, and is a driving factor for various severe liver diseases. These multi-factorial and multi-timescale changes are observed in different clinical studies, but these studies have not been integrated into a unified framework. In this study, we aim to present such a unified framework in the form of a dynamic mathematical model.

METHODS: For model training and validation, we collected data for dietary or drug-induced interventions aimed at reducing or increasing liver fat. The model was formulated using ordinary differential equations (ODEs) and the mathematical analysis, model simulation, model formulation and the model parameter estimation were all performed in MATLAB.

RESULTS: Our mathematical model describes accumulation of fat in the liver and predicts changes in lipid fluxes induced by both dietary and drug interventions. The model is validated using data from a wide range of drug and dietary intervention studies and can predict both short-term (days) and long-term (weeks) changes in liver fat. Importantly, the model computes the contribution of each individual lipid flux to the total liver fat dynamics. Furthermore, the model can be combined with an established bodyweight model, to simulate even longer scenarios (years), also including the effects of insulin resistance and body weight. To help prepare for corresponding eHealth applications, we also present a way to visualize the simulated changes, using dynamically changing lipid droplets, seen in images of liver biopsies.

CONCLUSION: In conclusion, we believe that the minimal model presented herein might be a useful tool for future applications, and to further integrate and understand data regarding changes in dietary and drug induced changes in ectopic TAG in the liver. With further development and validation, the minimal model could be used as a disease progression model for steatosis.

PMID:38754305 | DOI:10.1016/j.clnu.2024.05.017

Mol Cancer. 2024 May 16;23(1):105. doi: 10.1186/s12943-024-02010-1.

ABSTRACT

BACKGROUND: The main drawback of BRAF/MEK inhibitors (BRAF/MEKi)-based targeted therapy in the management of BRAF-mutated cutaneous metastatic melanoma (MM) is the development of therapeutic resistance. We aimed to assess in this context the role of mTORC2, a signaling complex defined by the presence of the essential RICTOR subunit, regarded as an oncogenic driver in several tumor types, including MM.

METHODS: After analyzing The Cancer Genome Atlas MM patients' database to explore both overall survival and molecular signatures as a function of intra-tumor RICTOR levels, we investigated the effects of RICTOR downregulation in BRAFV600E MM cell lines on their response to BRAF/MEKi. We performed proteomic screening to identify proteins modulated by changes in RICTOR expression, and Seahorse analysis to evaluate the effects of RICTOR depletion on mitochondrial respiration. The combination of BRAFi with drugs targeting proteins and processes emerged in the proteomic screening was carried out on RICTOR-deficient cells in vitro and in a xenograft setting in vivo.

RESULTS: Low RICTOR levels in BRAF-mutated MM correlate with a worse clinical outcome. Gene Set Enrichment Analysis of low-RICTOR tumors display gene signatures suggestive of activation of the mitochondrial Electron Transport Chain (ETC) energy production. RICTOR-deficient BRAFV600E cells are intrinsically tolerant to BRAF/MEKi and anticipate the onset of resistance to BRAFi upon prolonged drug exposure. Moreover, in drug-naïve cells we observed a decline in RICTOR expression shortly after BRAFi exposure. In RICTOR-depleted cells, both mitochondrial respiration and expression of nicotinamide phosphoribosyltransferase (NAMPT) are enhanced, and their pharmacological inhibition restores sensitivity to BRAFi.

CONCLUSIONS: Our work unveils an unforeseen tumor-suppressing role for mTORC2 in the early adaptation phase of BRAFV600E melanoma cells to targeted therapy and identifies the NAMPT-ETC axis as a potential therapeutic vulnerability of low RICTOR tumors. Importantly, our findings indicate that the evaluation of intra-tumor RICTOR levels has a prognostic value in metastatic melanoma and may help to guide therapeutic strategies in a personalized manner.

PMID:38755661 | DOI:10.1186/s12943-024-02010-1

Nat Immunol. 2024 May 16. doi: 10.1038/s41590-024-01864-3. Online ahead of print.

NO ABSTRACT

PMID:38755325 | DOI:10.1038/s41590-024-01864-3

Nat Commun. 2024 May 16;15(1):4177. doi: 10.1038/s41467-024-48556-y.

ABSTRACT

Plasma RNAemia, delayed antibody responses and inflammation predict COVID-19 outcomes, but the mechanisms underlying these immunovirological patterns are poorly understood. We profile 782 longitudinal plasma samples from 318 hospitalized patients with COVID-19. Integrated analysis using k-means reveals four patient clusters in a discovery cohort: mechanically ventilated critically-ill cases are subdivided into good prognosis and high-fatality clusters (reproduced in a validation cohort), while non-critical survivors segregate into high and low early antibody responders. Only the high-fatality cluster is enriched for transcriptomic signatures associated with COVID-19 severity, and each cluster has distinct RBD-specific antibody elicitation kinetics. Both critical and non-critical clusters with delayed antibody responses exhibit sustained IFN signatures, which negatively correlate with contemporaneous RBD-specific IgG levels and absolute SARS-CoV-2-specific B and CD4+ T cell frequencies. These data suggest that the "Interferon paradox" previously described in murine LCMV models is operative in COVID-19, with excessive IFN signaling delaying development of adaptive virus-specific immunity.

PMID:38755196 | DOI:10.1038/s41467-024-48556-y

Nat Commun. 2024 May 16;15(1):4176. doi: 10.1038/s41467-024-48504-w.

ABSTRACT

SETD3 is an essential host factor for the replication of a variety of enteroviruses that specifically interacts with viral protease 2A. However, the interaction between SETD3 and the 2A protease has not been fully characterized. Here, we use X-ray crystallography and cryo-electron microscopy to determine the structures of SETD3 complexed with the 2A protease of EV71 to 3.5 Å and 3.1 Å resolution, respectively. We find that the 2A protease occupies the V-shaped central cleft of SETD3 through two discrete sites. The relative positions of the two proteins vary in the crystal and cryo-EM structures, showing dynamic binding. A biolayer interferometry assay shows that the EV71 2A protease outcompetes actin for SETD3 binding. We identify key 2A residues involved in SETD3 binding and demonstrate that 2A's ability to bind SETD3 correlates with EV71 production in cells. Coimmunoprecipitation experiments in EV71 infected and 2A expressing cells indicate that 2A interferes with the SETD3-actin complex, and the disruption of this complex reduces enterovirus replication. Together, these results reveal the molecular mechanism underlying the interplay between SETD3, actin, and viral 2A during virus replication.

PMID:38755176 | DOI:10.1038/s41467-024-48504-w

Sci Data. 2024 May 16;11(1):503. doi: 10.1038/s41597-024-03324-x.

ABSTRACT

Nanomaterials hold great promise for improving our society, and it is crucial to understand their effects on biological systems in order to enhance their properties and ensure their safety. However, the lack of consistency in experimental reporting, the absence of universally accepted machine-readable metadata standards, and the challenge of combining such standards hamper the reusability of previously produced data for risk assessment. Fortunately, the research community has responded to these challenges by developing minimum reporting standards that address several of these issues. By converting twelve published minimum reporting standards into a machine-readable representation using FAIR maturity indicators, we have created a machine-friendly approach to annotate and assess datasets' reusability according to those standards. Furthermore, our NanoSafety Data Reusability Assessment (NSDRA) framework includes a metadata generator web application that can be integrated into experimental data management, and a new web application that can summarize the reusability of nanosafety datasets for one or more subsets of maturity indicators, tailored to specific computational risk assessment use cases. This approach enhances the transparency, communication, and reusability of experimental data and metadata. With this improved FAIR approach, we can facilitate the reuse of nanosafety research for exploration, toxicity prediction, and regulation, thereby advancing the field and benefiting society as a whole.

PMID:38755173 | DOI:10.1038/s41597-024-03324-x

Science. 2024 May 17;384(6697):744. doi: 10.1126/science.adp9584. Epub 2024 May 16.

ABSTRACT

Trailblazing biochemist and author.

PMID:38753772 | DOI:10.1126/science.adp9584

Plant Physiol. 2024 May 16:kiae286. doi: 10.1093/plphys/kiae286. Online ahead of print.

ABSTRACT

Sclerotinia disease is one of the most devastating fungal diseases worldwide, as it reduces the yields of many economically important crops. Pathogen-secreted effectors play crucial roles in infection processes. However, key effectors of Ciboria shiraiana, the pathogen primarily responsible for sclerotinia disease in mulberry (Morus spp.), remain poorly understood. In this study, we identified and functionally characterized the effector Cs02526 in C. shiraiana and found that Cs02526 could induce cell deathin a variety of plants. Moreover, Cs02526-induced cell death was mediated by the central immune regulator BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1 (BAK1), dependent on a 67-amino acid fragment. Notably, Cs02526 homologues were widely distributed in hemibiotrophic and necrotrophic phytopathogenic fungi, but the homologues failed to induce cell death in plants. Pre-treatment of plants with recombinant Cs02526 protein enhanced resistance against both C. shiraiana and Sclerotinia sclerotiorum. Furthermore, the pathogenicity of C. shiraiana was diminished upon spraying plants with synthetic dsRNA-Cs02526. In conclusion, our findings highlight the cell death-inducing effector Cs02526 as a potential target for future biological control strategies against plant diseases.

PMID:38753366 | DOI:10.1093/plphys/kiae286

J Biol Phys. 2024 May 16. doi: 10.1007/s10867-024-09657-3. Online ahead of print.

ABSTRACT

Studying the calcium dynamics within a fibroblast cell individually has provided only a restricted understanding of its functions. However, research efforts focusing on systems biology approaches for such investigations have been largely neglected by researchers until now. Fibroblast cells rely on signaling from calcium ( C a 2 + ) and nitric oxide (NO) to maintain their physiological functions and structural stability. Various studies have demonstrated the correlation between NO and the control of C a 2 + dynamics in cells. However, there is currently no existing model to assess the disruptions caused by various factors in regulatory dynamics, potentially resulting in diverse fibrotic disorders. A mathematical model has been developed to investigate the effects of changes in parameters such as buffer, receptor, sarcoplasmic endoplasmic reticulum C a 2 + -ATPase (SERCA) pump, and source influx on the regulation and dysregulation of spatiotemporal calcium and NO dynamics in fibroblast cells. This model is based on a system of reaction-diffusion equations, and numerical simulations are conducted using the finite element method. Disturbances in key processes related to calcium and nitric oxide, including source influx, buffer mechanism, SERCA pump, and inositol trisphosphate ( I P 3 ) receptor, may contribute to deregulation in the calcium and NO dynamics within fibroblasts. The findings also provide new insights into the extent and severity of disorders resulting from alterations in various parameters, potentially leading to deregulation and the development of fibrotic disease.

PMID:38753214 | DOI:10.1007/s10867-024-09657-3

OMICS. 2024 May 15. doi: 10.1089/omi.2024.0047. Online ahead of print.

ABSTRACT

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

PMID:38752932 | DOI:10.1089/omi.2024.0047

mSystems. 2024 May 16:e0138523. doi: 10.1128/msystems.01385-23. Online ahead of print.

ABSTRACT

A dysfunction of human host genes and proteins in coronavirus infectious disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a key factor impacting clinical symptoms and outcomes. Yet, a detailed understanding of human host immune responses is still incomplete. Here, we applied RNA sequencing to 94 samples of COVID-19 patients with and without hematological tumors as well as COVID-19 uninfected non-tumor individuals to obtain a comprehensive transcriptome landscape of both hematological tumor patients and non-tumor individuals. In our analysis, we further accounted for the human-SARS-CoV-2 protein interactome, human protein interactome, and human protein complex subnetworks to understand the mechanisms of SARS-CoV-2 infection and host immune responses. Our data sets enabled us to identify important SARS-CoV-2 (non-)targeted differentially expressed genes and complexes post-SARS-CoV-2 infection in both hematological tumor and non-tumor individuals. We found several unique differentially expressed genes, complexes, and functions/pathways such as blood coagulation (APOE, SERPINE1, SERPINE2, and TFPI), lipoprotein particle remodeling (APOC2, APOE, and CETP), and pro-B cell differentiation (IGHM, VPREB1, and IGLL1) during COVID-19 infection in patients with hematological tumors. In particular, APOE, a gene that is associated with both blood coagulation and lipoprotein particle remodeling, is not only upregulated in hematological tumor patients post-SARS-CoV-2 infection but also significantly expressed in acute dead patients with hematological tumors, providing clues for the design of future therapeutic strategies specifically targeting COVID-19 in patients with hematological tumors. Our data provide a rich resource for understanding the specific pathogenesis of COVID-19 in immunocompromised patients, such as those with hematological malignancies, and developing effective therapeutics for COVID-19.

IMPORTANCE: A majority of previous studies focused on the characterization of coronavirus infectious disease 2019 (COVID-19) disease severity in people with normal immunity, while the characterization of COVID-19 in immunocompromised populations is still limited. Our study profiles changes in the transcriptome landscape post-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in hematological tumor patients and non-tumor individuals. Furthermore, our integrative and comparative systems biology analysis of the interactome, complexome, and transcriptome provides new insights into the tumor-specific pathogenesis of COVID-19. Our findings confirm that SARS-CoV-2 potentially tends to target more non-functional host proteins to indirectly affect host immune responses in hematological tumor patients. The identified unique genes, complexes, functions/pathways, and expression patterns post-SARS-CoV-2 infection in patients with hematological tumors increase our understanding of how SARS-CoV-2 manipulates the host molecular mechanism. Our observed differential genes/complexes and clinical indicators of normal/long infection and deceased COVID-19 patients provide clues for understanding the mechanism of COVID-19 progression in hematological tumors. Finally, our study provides an important data resource that supports the increasing value of the application of publicly accessible data sets to public health.

PMID:38752789 | DOI:10.1128/msystems.01385-23

Sci Rep. 2024 May 15;14(1):11172. doi: 10.1038/s41598-024-61690-3.

ABSTRACT

A significant number of pregnancies are lost in the first trimester and 1-2% are ectopic pregnancies (EPs). Early pregnancy loss in general can cause significant morbidity with bleeding or infection, while EPs are the leading cause of maternal mortality in the first trimester. Symptoms of pregnancy loss and EP are very similar (including pain and bleeding); however, these symptoms are also common in live normally sited pregnancies (LNSP). To date, no biomarkers have been identified to differentiate LNSP from pregnancies that will not progress beyond early gestation (non-viable or EPs), defined together as combined adverse outcomes (CAO). In this study, we present a novel machine learning pipeline to create prediction models that identify a composite biomarker to differentiate LNSP from CAO in symptomatic women. This prospective cohort study included 370 participants. A single blood sample was prospectively collected from participants on first emergency presentation prior to final clinical diagnosis of pregnancy outcome: LNSP, miscarriage, pregnancy of unknown location (PUL) or tubal EP (tEP). Miscarriage, PUL and tEP were grouped together into a CAO group. Human chorionic gonadotrophin β (β-hCG) and progesterone concentrations were measured in plasma. Serum samples were subjected to untargeted metabolomic profiling. The cohort was randomly split into train and validation data sets, with the train data set subjected to variable selection. Nine metabolite signals were identified as key discriminators of LNSP versus CAO. Random forest models were constructed using stable metabolite signals alone, or in combination with plasma hormone concentrations and demographic data. When comparing LNSP with CAO, a model with stable metabolite signals only demonstrated a modest predictive accuracy (0.68), which was comparable to a model of β-hCG and progesterone (0.71). The best model for LNSP prediction comprised stable metabolite signals and hormone concentrations (accuracy = 0.79). In conclusion, serum metabolite levels and biochemical markers from a single blood sample possess modest predictive utility in differentiating LNSP from CAO pregnancies upon first presentation, which is improved by variable selection and combination using machine learning. A diagnostic test to confirm LNSP and thus exclude pregnancies affecting maternal morbidity and potentially life-threatening outcomes would be invaluable in emergency situations.

PMID:38750192 | DOI:10.1038/s41598-024-61690-3

Nat Commun. 2024 May 15;15(1):4109. doi: 10.1038/s41467-024-48132-4.

ABSTRACT

Label-free detection of multiple analytes in a high-throughput fashion has been one of the long-sought goals in biosensing applications. Yet, for all-optical approaches, interfacing state-of-the-art label-free techniques with microfluidics tools that can process small volumes of sample with high throughput, and with surface chemistry that grants analyte specificity, poses a critical challenge to date. Here, we introduce an optofluidic platform that brings together state-of-the-art digital holography with PDMS microfluidics by using supported lipid bilayers as a surface chemistry building block to integrate both technologies. Specifically, this platform fingerprints heterogeneous biological nanoparticle populations via a multiplexed label-free immunoaffinity assay with single particle sensitivity. First, we characterise the robustness and performance of the platform, and then apply it to profile four distinct ovarian cell-derived extracellular vesicle populations over a panel of surface protein biomarkers, thus developing a unique biomarker fingerprint for each cell line. We foresee that our approach will find many applications where routine and multiplexed characterisation of biological nanoparticles are required.

PMID:38750038 | DOI:10.1038/s41467-024-48132-4

Bioinformatics. 2024 May 15:btae318. doi: 10.1093/bioinformatics/btae318. Online ahead of print.

ABSTRACT

MOTIVATION: The identification of minimal genetic interventions that modulate metabolic processes constitutes one of the most relevant applications of genome-scale metabolic models (GEMs). The concept of Minimal Cut Sets (MCSs) and its extension at the gene level, genetic Minimal Cut Sets (gMCSs), have attracted increasing interest in the field of Systems Biology to address this task. Different computational tools have been developed to calculate MCSs and gMCSs using both commercial and open-source software.

RESULTS: Here, we present gMCSpy, an efficient Python package to calculate gMCSs in GEMs using both commercial and non-commercial optimization solvers. We show that gMCSpy substantially overperforms our previous computational tool GMCS, which exclusively relied on commercial software. Moreover, we compared gMCSpy with recently published competing algorithms in the literature, finding significant improvements in both accuracy and computation time. All these advances make gMCSpy an attractive tool for researchers in the field of Systems Biology for different applications in health and biotechnology.

AVAILABILITY: The Python package gMCSpy can be accessed at: https://github.com/PlanesLab/gMCSpy.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:38748994 | DOI:10.1093/bioinformatics/btae318

Cell Rep. 2024 May 14;43(5):114219. doi: 10.1016/j.celrep.2024.114219. Online ahead of print.

ABSTRACT

Defining the molecular networks orchestrating human brain formation is crucial for understanding neurodevelopment and neurological disorders. Challenges in acquiring early brain tissue have incentivized the use of three-dimensional human pluripotent stem cell (hPSC)-derived neural organoids to recapitulate neurodevelopment. To elucidate the molecular programs that drive this highly dynamic process, here, we generate a comprehensive trans-omic map of the phosphoproteome, proteome, and transcriptome of the exit of pluripotency and neural differentiation toward human cerebral organoids (hCOs). These data reveal key phospho-signaling events and their convergence on transcriptional factors to regulate hCO formation. Comparative analysis with developing human and mouse embryos demonstrates the fidelity of our hCOs in modeling embryonic brain development. Finally, we demonstrate that biochemical modulation of AKT signaling can control hCO differentiation. Together, our data provide a comprehensive resource to study molecular controls in human embryonic brain development and provide a guide for the future development of hCO differentiation protocols.

PMID:38748874 | DOI:10.1016/j.celrep.2024.114219

Sci Adv. 2024 May 17;10(20):eadn5143. doi: 10.1126/sciadv.adn5143. Epub 2024 May 15.

ABSTRACT

Marine heterotrophic prokaryotes primarily take up ambient substrates using transporters. The patterns of transporters targeting particular substrates shape the ecological role of heterotrophic prokaryotes in marine organic matter cycles. Here, we report a size-fractionated pattern in the expression of prokaryotic transporters throughout the oceanic water column due to taxonomic variations, revealed by a multi-"omics" approach targeting ATP-binding cassette (ABC) transporters and TonB-dependent transporters (TBDTs). Substrate specificity analyses showed that marine SAR11, Rhodobacterales, and Oceanospirillales use ABC transporters to take up organic nitrogenous compounds in the free-living fraction, while Alteromonadales, Bacteroidetes, and Sphingomonadales use TBDTs for carbon-rich organic matter and metal chelates on particles. The expression of transporter proteins also supports distinct lifestyles of deep-sea prokaryotes. Our results suggest that transporter divergency in organic matter assimilation reflects a pronounced niche separation in the prokaryote-mediated organic matter cycles.

PMID:38748788 | DOI:10.1126/sciadv.adn5143

PLoS Comput Biol. 2024 May 15;20(5):e1012106. doi: 10.1371/journal.pcbi.1012106. Online ahead of print.

ABSTRACT

Contrast transport models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.

PMID:38748755 | DOI:10.1371/journal.pcbi.1012106

Proc Natl Acad Sci U S A. 2024 May 21;121(21):e2400018121. doi: 10.1073/pnas.2400018121. Epub 2024 May 15.

ABSTRACT

Hybridization blurs species boundaries and leads to intertwined lineages resulting in reticulate evolution. Polyploidy, the outcome of whole genome duplication (WGD), has more recently been implicated in promoting and facilitating hybridization between polyploid species, potentially leading to adaptive introgression. However, because polyploid lineages are usually ephemeral states in the evolutionary history of life it is unclear whether WGD-potentiated hybridization has any appreciable effect on their diploid counterparts. Here, we develop a model of cytotype dynamics within mixed-ploidy populations to demonstrate that polyploidy can in fact serve as a bridge for gene flow between diploid lineages, where introgression is fully or partially hampered by the species barrier. Polyploid bridges emerge in the presence of triploid organisms, which despite critically low levels of fitness, can still allow the transfer of alleles between diploid states of independently evolving mixed-ploidy species. Notably, while marked genetic divergence prevents polyploid-mediated interspecific gene flow, we show that increased recombination rates can offset these evolutionary constraints, allowing a more efficient sorting of alleles at higher-ploidy levels before introgression into diploid gene pools. Additionally, we derive an analytical approximation for the rate of gene flow at the tetraploid level necessary to supersede introgression between diploids with nonzero introgression rates, which is especially relevant for plant species complexes, where interspecific gene flow is ubiquitous. Altogether, our results illustrate the potential impact of polyploid bridges on the (re)distribution of genetic material across ecological communities during evolution, representing a potential force behind reticulation.

PMID:38748576 | DOI:10.1073/pnas.2400018121

Anal Bioanal Chem. 2024 May 15. doi: 10.1007/s00216-024-05331-8. Online ahead of print.

ABSTRACT

Glycans participate in a vast number of recognition systems in diverse organisms in health and in disease. However, glycans cannot be sequenced because there is no sequencer technology that can fully characterize them. There is no "template" for replicating glycans as there are for amino acids and nucleic acids. Instead, glycans are synthesized by a complicated orchestration of multitudes of glycosyltransferases and glycosidases. Thus glycans can vary greatly in structure, but they are not genetically reproducible and are usually isolated in minute amounts. To characterize (sequence) the glycome (defined as the glycans in a particular organism, tissue, cell, or protein), glycosylation pathway prediction using in silico methods based on glycogene expression data, and glycosylation simulations have been attempted. Since many of the mammalian glycogenes have been identified and cloned, it has become possible to predict the glycan biosynthesis pathway in these systems. By then incorporating systems biology and bioprocessing technologies to these pathway models, given the right enzymatic parameters including enzyme and substrate concentrations and kinetic reaction parameters, it is possible to predict the potentially synthesized glycans in the pathway. This review presents information on the data resources that are currently available to enable in silico simulations of glycosylation and related pathways. Then some of the software tools that have been developed in the past to simulate and analyze glycosylation pathways will be described, followed by a summary and vision for the future developments and research directions in this area.

PMID:38748247 | DOI:10.1007/s00216-024-05331-8

Cell Mol Life Sci. 2024 May 15;81(1):217. doi: 10.1007/s00018-024-05202-6.

ABSTRACT

The vertebrate sense of taste allows rapid assessment of the nutritional quality and potential presence of harmful substances prior to ingestion. Among the five basic taste qualities, salty, sour, sweet, umami, and bitter, bitterness is associated with the presence of putative toxic substances and elicits rejection behaviors in a wide range of animals including humans. However, not all bitter substances are harmful, some are thought to be health-beneficial and nutritious. Among those compound classes that elicit a bitter taste although being non-toxic and partly even essential for humans are bitter peptides and L-amino acids. Using functional heterologous expression assays, we observed that the 5 dominant human bitter taste receptors responsive to bitter peptides and amino acids are activated by bile acids, which are notorious for their extreme bitterness. We further demonstrate that the cross-reactivity of bitter taste receptors for these two different compound classes is evolutionary conserved and can be traced back to the amphibian lineage. Moreover, we show that the cross-detection by some receptors relies on "structural mimicry" between the very bitter peptide L-Trp-Trp-Trp and bile acids, whereas other receptors exhibit a phylogenetic conservation of this trait. As some bile acid-sensitive bitter taste receptor genes fulfill dual-roles in gustatory and non-gustatory systems, we suggest that the phylogenetic conservation of the rather surprising cross-detection of the two substance classes could rely on a gene-sharing-like mechanism in which the non-gustatory function accounts for the bitter taste response to amino acids and peptides.

PMID:38748186 | DOI:10.1007/s00018-024-05202-6