Front Microbiol. 2024 Aug 8;15:1395751. doi: 10.3389/fmicb.2024.1395751. eCollection 2024.

ABSTRACT

Microwaves have become an essential part of the modern kitchen, but their potential as a reservoir for bacterial colonization and the microbial composition within them remain largely unexplored. In this study, we investigated the bacterial communities in microwave ovens and compared the microbial composition of domestic microwaves, microwaves used in shared large spaces, and laboratory microwaves, using next-generation sequencing and culturing techniques. The microwave oven bacterial population was dominated by Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes, similar to the bacterial composition of human skin. Comparison with other environments revealed that the bacterial composition of domestic microwaves was similar to that of kitchen surfaces, whereas laboratory microwaves had a higher abundance of taxa known for their ability to withstand microwave radiation, high temperatures and desiccation. These results suggest that different selective pressures, such as human contact, nutrient availability and radiation levels, may explain the differences observed between domestic and laboratory microwaves. Overall, this study provides valuable insights into microwave ovens bacterial communities and their potential biotechnological applications.

PMID:39176272 | PMC:PMC11338789 | DOI:10.3389/fmicb.2024.1395751

Hepatobiliary Surg Nutr. 2024 Aug 1;13(4):650-661. doi: 10.21037/hbsn-23-562. Epub 2024 Apr 18.

ABSTRACT

BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma (HCC) poses a significant global health burden and ranks as the fifth most prevalent cancer on a global scale. Hepatitis C virus (HCV) infection remains one of the major risk factors for HCC development. HCC is a heterogeneous disease, and the development of HCC caused by HCV is intricate and involves various factors, including genetic susceptibility, viral factors, immune response due to chronic inflammation, alcohol abuse, and metabolic dysfunction associated with fatty liver disease. In this review, we provide a comprehensive and updated review of research on the genetics and epigenetic mechanisms implicated in developing HCC associated with HCV infection. We also discuss the potential translational implications, including novel biomarkers and drugs for treatment.

METHODS: A comprehensive literature search was conducted in June 2023 in PubMed and Embase databases.

KEY CONTENT AND FINDINGS: Recent findings indicate that a variety of genetic and epigenetic processes are involved in the pathogenesis and continue to exist even after the complete elimination of HCV. The deregulation of the epigenome has been identified as a significant factor in the deletrious effects of liver disease, especially during the initial stages when genetic alterations are uncommon. The enduring "epigenetic memory" of gene expression is believed to be regulated by epigenetic mechanisms, indicating that alterations caused by HCV infection continue to exist and are linked to the risk of development of liver cancer even after successful treatment. Systems biology analytical methods will be required to delineate the magnitude and significance of both genetic and epigenomic alterations in tumor evolution.

CONCLUSIONS: By facilitating a more profound understanding of these aspects, this will ultimately foster the advancement of novel therapies and ultimately improve outcomes for patients.

PMID:39175720 | PMC:PMC11336528 | DOI:10.21037/hbsn-23-562

J Pharm Anal. 2024 Jul;14(7):100973. doi: 10.1016/j.jpha.2024.100973. Epub 2024 Mar 28.

ABSTRACT

Polygala tenuifolia, commonly known as Yuanzhi (YZ) in Chinese, has been shown to possess anti-insomnia properties. However, the material basis and the mechanism underlying its sedative-hypnotic effects remain unclear. Herein, we investigated the active components and neurochemical mechanism of YZ extracts using liquid chromatography tandem mass spectrometry (LC-MS/MS)-based pharmacometabolomics and mass spectrometry imaging (MSI)-based spatial resolved metabolomics. According to the results, 17 prototypes out of 101 ingredients in the YZ extract were detected in both the plasma and brain, which might be the major components contributing to the sedative-hypnotic effects. Network pharmacology analysis revealed that these prototypes may exert their effects through neuroactive ligand-receptor interaction, serotonergic synapse, dopaminergic synapse, and dopaminergic synapse, among other pathways. LC-MS/MS-based targeted metabolomics and Western blot (WB) revealed that tryptophan-serotonin-melatonin (Trp-5-HT-Mel) and tyrosine-norepinephrine-adrenaline (Tyr-Ne-Ad) are the key regulated pathways. Dopa decarboxylase (DDC) upregulation and phenylethanolamine N-methyltransferase (PNMT) downregulation further confirmed these pathways. Furthermore, MSI-based spatially resolved metabolomics revealed notable alterations in 5-HT in the pineal gland (PG), and Ad in the brainstem, including the middle brain (MB), pons (PN), and hypothalamus (HY). In summary, this study illustrates the efficacy of an integrated multidimensional metabolomics approach in unraveling the sedative-hypnotic effects and neurochemical mechanisms of a Chinese herbal medicine, YZ.

PMID:39175609 | PMC:PMC11340588 | DOI:10.1016/j.jpha.2024.100973

Cell Syst. 2024 Aug 21;15(8):738-752.e5. doi: 10.1016/j.cels.2024.07.007.

ABSTRACT

Cellular longevity is regulated by both genetic and environmental factors. However, the interactions of these factors in the context of aging remain largely unclear. Here, we formulate a mathematical model for dynamic glucose modulation of a core gene circuit in yeast aging, which not only guided the design of pro-longevity interventions but also revealed the theoretical principles underlying these interventions. We introduce the dynamical systems theory to capture two general means for promoting longevity-the creation of a stable fixed point in the "healthy" state of the cell and the "dynamic stabilization" of the system around this healthy state through environmental oscillations. Guided by the model, we investigate how both of these can be experimentally realized by dynamically modulating environmental glucose levels. The results establish a paradigm for theoretically analyzing the trajectories and perturbations of aging that can be generalized to aging processes in diverse cell types and organisms.

PMID:39173586 | DOI:10.1016/j.cels.2024.07.007

Comput Biol Med. 2024 Aug 21;181:109029. doi: 10.1016/j.compbiomed.2024.109029. Online ahead of print.

ABSTRACT

Tuberculosis (TB) is an infectious disease that primarily affects the lungs of humans and accounts for Mycobacterium tuberculosis (Mtb) bacteria as the etiologic agent. In this study, we introduce a computational framework designed to identify the important chemical features crucial for the effective inhibition of Mtb β-CAs. Through applying a mechanistic model, we elucidated the essential features pivotal for robust inhibition. Using this model, we engineered molecules that exhibit potent inhibitory activity and introduce relevant novel chemistry. The designed molecules were prioritized for synthesis based on their predicted pKi values via the QSAR (Quantitative Structure-Activity Relationship) model. All the rationally designed and synthesized compounds were evaluated in vitro against different carbonic anhydrase isoforms expressed from the pathogen Mtb; moreover, the off-target and widely human-expressed CA I and II were also evaluated. Among the reported derivatives, 2, 4, and 5 demonstrated the most valuable in vitro activity, resulting in promising candidates for the treatment of TB infection. All the synthesized molecules exhibited favorable pharmacokinetic and toxicological profiles based on in silico predictions. Docking analysis confirmed that the zinc-binding groups bind effectively into the catalytic triad of the Mtb β-Cas, supporting the in vitro outcomes with these binding interactions. Furthermore, molecules with good prediction accuracies according to previously established mechanistic and QSAR models were utilized to delve deeper into the realm of systems biology to understand their mechanism in combating tuberculotic pathogenesis. The results pointed to the key involvement of the compounds in modulating immune responses via NF-κβ1, SRC kinase, and TNF-α to modulate granuloma formation and clearance via T cells. This dual action, in which the pathogen's enzyme is inhibited while modulating the human immune machinery, represents a paradigm shift toward more effective and comprehensive treatment approaches for combating tuberculosis.

PMID:39173489 | DOI:10.1016/j.compbiomed.2024.109029

Nucleic Acids Res. 2024 Aug 23:gkae697. doi: 10.1093/nar/gkae697. Online ahead of print.

ABSTRACT

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs) (1-3). Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.

PMID:39175109 | DOI:10.1093/nar/gkae697

Virol J. 2024 Aug 22;21(1):193. doi: 10.1186/s12985-024-02456-1.

ABSTRACT

BACKGROUND: Emerging zoonotic diseases arise from cross-species transmission events between wild or domesticated animals and humans, with bats being one of the major reservoirs of zoonotic viruses. Viral metagenomics has led to the discovery of many viruses, but efforts have mainly been focused on some areas of the world and on certain viral families.

METHODS: We set out to describe full-length genomes of new picorna-like viruses by collecting feces from hundreds of bats captured in different regions of Spain. Viral sequences were obtained by high-throughput Illumina sequencing and analyzed phylogenetically to classify them in the context of known viruses. Linear discriminant analysis (LDA) was performed to infer likely hosts based on genome composition.

RESULTS: We found five complete or nearly complete genomes belonging to the family Picornaviridae, including a new species of the subfamily Ensavirinae. LDA suggested that these were true vertebrate viruses, rather than viruses from the bat diet. Some of these viruses were related to picornaviruses previously found in other bat species from distant geographical regions. We also found a calhevirus genome that most likely belongs to a proposed new family within the order Picornavirales, and for which genome composition analysis suggested a plant host.

CONCLUSIONS: Our findings describe new picorna-like viral species and variants circulating in the Iberian Peninsula, illustrate the wide geographical distribution and interspecies transmissibility of picornaviruses, and suggest new hosts for calheviruses.

PMID:39175061 | DOI:10.1186/s12985-024-02456-1

Sci Rep. 2024 Aug 22;14(1):19553. doi: 10.1038/s41598-024-70687-x.

ABSTRACT

The nitidulid beetle Carpophilus truncatus is rapidly becoming a major pest of nut crops around the world. This insect first infested Australian almonds in 2013 and has since escalated to be the preeminent insect pest for the industry. Data pertaining to C. truncatus distribution are scant, but without awareness of its origin, distribution, and ecological factors that influence distribution, efforts to understand and manage the insect as a pest are stymied. Here, we employ an integrative approach to gain a multifaceted understanding of the distribution of C. truncatus in Australia. Methods employed were (1) reviewing historical records in insect collections to establish the presence of C. truncatus prior to commercial almond horticulture, (2) field trapping of insects to establish presence in regions of interest, (3) laboratory trials to determine the thermal limits of the organism, and (4) correlative species distribution modelling to describe its current distribution. We find that C. truncatus is more widespread across Australia than was previously known, with historical records preceding commercial almond production in Australia by a century. The methods developed in this study can be applied elsewhere in the world where C. truncatus is an emerging pest, or to novel pest species as they arise with increasing frequency in a globalised and warming world.

PMID:39174634 | DOI:10.1038/s41598-024-70687-x

Sci Data. 2024 Aug 22;11(1):906. doi: 10.1038/s41597-024-03673-7.

ABSTRACT

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph (KG) encompassing biological knowledge about RNAs gathered from more than 60 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.

PMID:39174566 | DOI:10.1038/s41597-024-03673-7

Schizophrenia (Heidelb). 2024 Aug 22;10(1):68. doi: 10.1038/s41537-024-00490-0.

ABSTRACT

Given the chronic nature of schizophrenia, it is important to examine age-specific prevalence and incidence to understand the scope of the burden of schizophrenia across the lifespan. Estimates of lifetime prevalence of schizophrenia have varied widely and have often relied upon community-based data estimates from over two decades ago, while more recent studies have shown considerable promise by leveraging pooled datasets. However, the validity of measures of schizophrenia, particularly new onset schizophrenia, has not been well studied in these large health databases. The current study examines prevalence and validity of incidence measures of new diagnoses of schizophrenia in 2019 using two U.S. administrative health databases: MarketScan, a national database of individuals receiving employer-sponsored commercial insurance (N = 16,365,997), and NYS Medicaid, a large state public insurance program (N = 4,414,153). Our results indicate that the prevalence of schizophrenia is over 10-fold higher, and the incidence two-fold higher, in the NYS Medicaid population compared to the MarketScan database. In addition, prevalence increased over the lifespan in the Medicaid population, but decreased in the employment based MarketScan database beginning in early adulthood. Incident measures of new diagnoses of schizophrenia had excellent validity, with positive predictive values and specificity exceeding 95%, but required a longer lookback period for Medicaid compared to MarketScan. Further work is needed to leverage these findings to develop robust clinical outcome predictors for new onset of schizophrenia within large administrative health data systems.

PMID:39174558 | DOI:10.1038/s41537-024-00490-0

NPJ Syst Biol Appl. 2024 Aug 22;10(1):94. doi: 10.1038/s41540-024-00412-x.

ABSTRACT

Ordinary differential equation (ODE) models are powerful tools for studying the dynamics of metabolic pathways. However, key challenges lie in constructing ODE models for metabolic pathways, specifically in our limited knowledge about which metabolite levels control which reaction rates. Identification of these regulatory networks is further complicated by the limited availability of relevant data. Here, we assess the conditions under which it is feasible to accurately identify regulatory networks in metabolic pathways by computationally fitting candidate network models with biochemical systems theory (BST) kinetics to data of varying quality. We use network motifs commonly found in metabolic pathways as a simplified testbed. Key features correlated with the level of difficulty in identifying the correct regulatory network were identified, highlighting the impact of sampling rate, data noise, and data incompleteness on structural uncertainty. We found that for a simple branched network motif with an equal number of metabolites and fluxes, identification of the correct regulatory network can be largely achieved and is robust to missing one of the metabolite profiles. However, with a bi-substrate bi-product reaction or more fluxes than metabolites in the network motif, the identification becomes more challenging. Stronger regulatory interactions and higher metabolite concentrations were found to be correlated with less structural uncertainty. These results could aid efforts to predict whether the true metabolic regulatory network can be computationally identified for a given stoichiometric network topology and dataset quality, thus helping to identify optimal measures to mitigate such identifiability issues in kinetic model development.

PMID:39174554 | DOI:10.1038/s41540-024-00412-x

Nat Commun. 2024 Aug 22;15(1):7222. doi: 10.1038/s41467-024-51694-y.

ABSTRACT

CRISPR/Cas-based transcriptional activators can be enhanced by intrinsically disordered regions (IDRs). However, the underlying mechanisms are still debatable. Here, we examine 12 well-known IDRs by fusing them to the dCas9-VP64 activator, of which only seven can augment activation, albeit independently of their phase separation capabilities. Moreover, modular domains (MDs), another class of multivalent molecules, though ineffective in enhancing dCas9-VP64 activity on their own, show substantial enhancement in transcriptional activation when combined with dCas9-VP64-IDR. By varying the number of gRNA binding sites and fusing dCas9-VP64 with different IDRs/MDs, we uncover that optimal, rather than maximal, cis-trans cooperativity enables the most robust activation. Finally, targeting promoter-enhancer pairs yields synergistic effects, which can be further amplified via enhancing chromatin interactions. Overall, our study develops a versatile platform for efficient gene activation and sheds important insights into CRIPSR-based transcriptional activators enhanced with multivalent molecules.

PMID:39174527 | DOI:10.1038/s41467-024-51694-y

Nat Commun. 2024 Aug 22;15(1):7027. doi: 10.1038/s41467-024-51216-w.

ABSTRACT

Expansion of the glutamine tract (poly-Q) in the protein huntingtin (HTT) causes the neurodegenerative disorder Huntington's disease (HD). Emerging evidence suggests that mutant HTT (mHTT) disrupts brain development. To gain mechanistic insights into the neurodevelopmental impact of human mHTT, we engineered male induced pluripotent stem cells to introduce a biallelic or monoallelic mutant 70Q expansion or to remove the poly-Q tract of HTT. The introduction of a 70Q mutation caused aberrant development of cerebral organoids with loss of neural progenitor organization. The early neurodevelopmental signature of mHTT highlighted the dysregulation of the protein coiled-coil-helix-coiled-coil-helix domain containing 2 (CHCHD2), a transcription factor involved in mitochondrial integrated stress response. CHCHD2 repression was associated with abnormal mitochondrial morpho-dynamics that was reverted upon overexpression of CHCHD2. Removing the poly-Q tract from HTT normalized CHCHD2 levels and corrected key mitochondrial defects. Hence, mHTT-mediated disruption of human neurodevelopment is paralleled by aberrant neurometabolic programming mediated by dysregulation of CHCHD2, which could then serve as an early interventional target for HD.

PMID:39174523 | DOI:10.1038/s41467-024-51216-w

Nat Commun. 2024 Aug 22;15(1):7233. doi: 10.1038/s41467-024-50873-1.

ABSTRACT

More than half of the world's rivers dry up periodically, but our understanding of the biological communities in dry riverbeds remains limited. Specifically, the roles of dispersal, environmental filtering and biotic interactions in driving biodiversity in dry rivers are poorly understood. Here, we conduct a large-scale coordinated survey of patterns and drivers of biodiversity in dry riverbeds. We focus on eight major taxa, including microorganisms, invertebrates and plants: Algae, Archaea, Bacteria, Fungi, Protozoa, Arthropods, Nematodes and Streptophyta. We use environmental DNA metabarcoding to assess biodiversity in dry sediments collected over a 1-year period from 84 non-perennial rivers across 19 countries on four continents. Both direct factors, such as nutrient and carbon availability, and indirect factors such as climate influence the local biodiversity of most taxa. Limited resource availability and prolonged dry phases favor oligotrophic microbial taxa. Co-variation among taxa, particularly Bacteria, Fungi, Algae and Protozoa, explain more spatial variation in community composition than dispersal or environmental gradients. This finding suggests that biotic interactions or unmeasured ecological and evolutionary factors may strongly influence communities during dry phases, altering biodiversity responses to global changes.

PMID:39174521 | DOI:10.1038/s41467-024-50873-1

PLoS One. 2024 Aug 22;19(8):e0307248. doi: 10.1371/journal.pone.0307248. eCollection 2024.

ABSTRACT

In the current study, systems biology approach was applied to get a deep insight regarding the regulatory mechanisms of Chromochloris zofingiensis under overall stress conditions. Meta-analysis was performed using p-values combination of differentially expressed genes. To identify the informative models related to stress conditions, two distinct weighted gene co-expression networks were constructed and preservation analyses were performed using medianRankand Zsummary algorithms. Moreover, functional enrichment analysis of non-preserved modules was performed to shed light on the biological performance of underlying genes in the non-preserved modules. In the next step, the gene regulatory networks between top hub genes of non-preserved modules and transcription factors were inferred using ensemble of trees algorithm. Results showed that the power of beta = 7 was the best soft-thresholding value to ensure a scale-free network, leading to the determination of 12 co-expression modules with an average size of 128 genes. Preservation analysis showed that the connectivity pattern of the six modules including the blue, black, yellow, pink, greenyellow, and turquoise changed during stress condition which defined as non-preserved modules. Examples of enriched pathways in non-preserved modules were Oxidative phosphorylation", "Vitamin B6 metabolism", and "Arachidonic acid metabolism". Constructed regulatory network between identified TFs and top hub genes of non-preserved module such as Cz06g10250, Cz03g12130 showed that some specific TFs such as C3H and SQUAMOSA promoter binding protein (SBP) specifically regulates the specific hubs. The current findings add substantially to our understanding of the stress responsive underlying mechanism of C. zofingiensis for future studies and metabolite production programs.

PMID:39172989 | DOI:10.1371/journal.pone.0307248

Science. 2024 Aug 23;385(6711):eabm6131. doi: 10.1126/science.abm6131. Epub 2024 Aug 23.

ABSTRACT

Impaired cerebral glucose metabolism is a pathologic feature of Alzheimer's disease (AD), with recent proteomic studies highlighting disrupted glial metabolism in AD. We report that inhibition of indoleamine-2,3-dioxygenase 1 (IDO1), which metabolizes tryptophan to kynurenine (KYN), rescues hippocampal memory function in mouse preclinical models of AD by restoring astrocyte metabolism. Activation of astrocytic IDO1 by amyloid β and tau oligomers increases KYN and suppresses glycolysis in an aryl hydrocarbon receptor-dependent manner. In amyloid and tau models, IDO1 inhibition improves hippocampal glucose metabolism and rescues hippocampal long-term potentiation in a monocarboxylate transporter-dependent manner. In astrocytic and neuronal cocultures from AD subjects, IDO1 inhibition improved astrocytic production of lactate and uptake by neurons. Thus, IDO1 inhibitors presently developed for cancer might be repurposed for treatment of AD.

PMID:39172838 | DOI:10.1126/science.abm6131

Science. 2024 Aug 23;385(6711):837. doi: 10.1126/science.adr4539. Epub 2024 Aug 22.

NO ABSTRACT

PMID:39172831 | DOI:10.1126/science.adr4539

Mol Biol Evol. 2024 Aug 22:msae176. doi: 10.1093/molbev/msae176. Online ahead of print.

ABSTRACT

Host-pathogen coevolution is defined as the reciprocal evolutionary changes in both species due to genotype x genotype (GxG) interactions at the genetic level determining the outcome and severity of infection. While co-analyses of host and pathogen genomes (co-GWAs) allow us to pinpoint the interacting genes, these do not reveal which host genotype(s) is/are resistant to which pathogen genotype(s). The knowledge of this so-called infection matrix is important for agriculture and medicine. Building on established theories of host-pathogen interactions, we here derive four novel indices capturing the characteristics of the infection matrix. These indices can be computed from full genome polymorphism data of randomly sampled uninfected hosts, as well as infected hosts and their pathogen strains. We use these indices in an Approximate Bayesian Computation method to pinpoint loci with relevant GxG interactions and to infer their underlying interaction matrix. In a combined SNP data set of 451 European humans and their infecting Hepatitis C Virus (HCV) strains and 503 uninfected individuals, we reveal a new human candidate gene for resistance to HCV and new virus mutations matching human genes. For two groups of significant human-HCV (GxG) associations, we infer a gene-for-gene infection matrix, which is commonly assumed to be typical of plant-pathogen interactions. Our model-based inference framework bridges theoretical models of GxG interactions with host and pathogen genomic data. It, therefore, paves the way for understanding the evolution of key GxG interactions underpinning HCV adaptation to the European human population after a recent expansion.

PMID:39172738 | DOI:10.1093/molbev/msae176

Bioinformatics. 2024 Aug 22:btae520. doi: 10.1093/bioinformatics/btae520. Online ahead of print.

ABSTRACT

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the cell state. However, its destructive nature prohibits measuring gene expression changes during dynamic processes such as embryogenesis. Although recent studies integrating scRNA-seq with lineage tracing have provided clonal insights between progenitor and mature cells, challenges remain. Because of their experimental nature, observations are sparse, and cells observed in the early state are not the exact progenitors of cells observed at later time points. To overcome these limitations, we developed LineageVAE, a novel computational methodology that utilizes deep learning based on the property that cells sharing barcodes have identical progenitors.

RESULTS: LineageVAE is a deep generative model that transforms scRNA-seq observations with identical lineage barcodes into sequential trajectories toward a common progenitor in a latent cell state space. This method enables the reconstruction of unobservable cell state transitions, historical transcriptomes, and regulatory dynamics at a single-cell resolution. Applied to hematopoiesis and reprogrammed fibroblast datasets, LineageVAE demonstrated its ability to restore backward cell state transitions and infer progenitor heterogeneity and transcription factor activity along differentiation trajectories.

AVAILABILITY AND IMPLEMENTATION: The LineageVAE model was implemented in Python using the PyTorch deep learning library. The code is available on GitHub at https://github.com/LzrRacer/LineageVAE/.

SUPPLEMENTARY INFORMATION: Available at Bioinformatics online.

PMID:39172488 | DOI:10.1093/bioinformatics/btae520

Brief Bioinform. 2024 Jul 25;25(5):bbae406. doi: 10.1093/bib/bbae406.

ABSTRACT

The tendency for cell fate to be robust to most perturbations, yet sensitive to certain perturbations raises intriguing questions about the existence of a key path within the underlying molecular network that critically determines distinct cell fates. Reprogramming and trans-differentiation clearly show examples of cell fate change by regulating only a few or even a single molecular switch. However, it is still unknown how to identify such a switch, called a master regulator, and how cell fate is determined by its regulation. Here, we present CAESAR, a computational framework that can systematically identify master regulators and unravel the resulting canalizing kernel, a key substructure of interconnected feedbacks that is critical for cell fate determination. We demonstrate that CAESAR can successfully predict reprogramming factors for de-differentiation into mouse embryonic stem cells and trans-differentiation of hematopoietic stem cells, while unveiling the underlying essential mechanism through the canalizing kernel. CAESAR provides a system-level understanding of how complex molecular networks determine cell fates.

PMID:39171985 | DOI:10.1093/bib/bbae406