Genome Biol. 2023 Dec 18;24(1):291. doi: 10.1186/s13059-023-03138-x.

ABSTRACT

Spatial omics technologies can help identify spatially organized biological processes, but existing computational approaches often overlook structural dependencies in the data. Here, we introduce Smoother, a unified framework that integrates positional information into non-spatial models via modular priors and losses. In simulated and real datasets, Smoother enables accurate data imputation, cell-type deconvolution, and dimensionality reduction with remarkable efficiency. In colorectal cancer, Smoother-guided deconvolution reveals plasma cell and fibroblast subtype localizations linked to tumor microenvironment restructuring. Additionally, joint modeling of spatial and single-cell human prostate data with Smoother allows for spatial mapping of reference populations with significantly reduced ambiguity.

PMID:38110959 | DOI:10.1186/s13059-023-03138-x

BMC Plant Biol. 2023 Dec 18;23(1):651. doi: 10.1186/s12870-023-04534-y.

ABSTRACT

BACKGROUND: Geminiviruses are DNA plant viruses that cause highly damaging diseases affecting crops worldwide. During the infection, geminiviruses hijack cellular processes, suppress plant defenses, and cause a massive reprogramming of the infected cells leading to major changes in the whole plant homeostasis. The advances in sequencing technologies allow the simultaneous analysis of multiple aspects of viral infection at a large scale, generating new insights into the molecular mechanisms underlying plant-virus interactions. However, an integrative study of the changes in the host transcriptome, small RNA profile and methylome during a geminivirus infection has not been performed yet. Using a time-scale approach, we aim to decipher the gene regulation in tomato in response to the infection with the geminivirus, tomato yellow leaf curl virus (TYLCV).

RESULTS: We showed that tomato undergoes substantial transcriptional and post-transcriptional changes upon TYLCV infection and identified the main altered regulatory pathways. Interestingly, although the principal plant defense-related processes, gene silencing and the immune response were induced, this cannot prevent the establishment of the infection. Moreover, we identified extra- and intracellular immune receptors as targets for the deregulated microRNAs (miRNAs) and established a network for those that also produced phased secondary small interfering RNAs (phasiRNAs). On the other hand, there were no significant genome-wide changes in tomato methylome at 14 days post infection, the time point at which the symptoms were general, and the amount of viral DNA had reached its maximum level, but we were able to identify differentially methylated regions that could be involved in the transcriptional regulation of some of the differentially expressed genes.

CONCLUSION: We have conducted a comprehensive and reliable study on the changes at transcriptional, post-transcriptional and epigenetic levels in tomato throughout TYLCV infection. The generated genomic information is substantial for understanding the genetic, molecular and physiological changes caused by TYLCV infection in tomato.

PMID:38110861 | DOI:10.1186/s12870-023-04534-y

Sci Rep. 2023 Dec 18;13(1):22472. doi: 10.1038/s41598-023-50156-7.

ABSTRACT

Hepatocellular carcinoma (HCC) is a prevalent malignancy and there is a lack of effective biomarkers for HCC diagnosis. Living organisms are complex, and different omics molecules interact with each other to implement various biological functions. Genomics and metabolomics, which are the top and bottom of systems biology, play an important role in HCC clinical management. Fatty acid metabolism is associated with malignancy, prognosis, and immune phenotype in cancer, which is a potential hallmark in malignant tumors. In this study, the genes and metabolites related to fatty acid metabolism were thoroughly investigated by a dynamic network construction algorithm named EWS-DDA for the early diagnosis and prognosis of HCC. Three gene ratios and eight metabolite ratios were identified by EWS-DDA as potential biomarkers for HCC clinical management. Further analysis using biological analysis, statistical analysis and document validation in the discovery and validation sets suggested that the selected potential biomarkers had great clinical prognostic value and helped to achieve effective early diagnosis of HCC. Experimental results suggested that in-depth evaluation of fatty acid metabolism from different omics viewpoints can facilitate the further understanding of pathological alterations associated with HCC characteristics, improving the performance of early diagnosis and clinical prognosis.

PMID:38110715 | DOI:10.1038/s41598-023-50156-7

NPJ Syst Biol Appl. 2023 Dec 18;9(1):63. doi: 10.1038/s41540-023-00324-2.

ABSTRACT

Assessing the mutagenicity of chemicals is an essential task in the drug development process. Usually, databases and other structured sources for AMES mutagenicity exist, which have been carefully and laboriously curated from scientific publications. As knowledge accumulates over time, updating these databases is always an overhead and impractical. In this paper, we first propose the problem of predicting the mutagenicity of chemicals from textual information in scientific publications. More simply, given a chemical and evidence in the natural language form from publications where the mutagenicity of the chemical is described, the goal of the model/algorithm is to predict if it is potentially mutagenic or not. For this, we first construct a golden standard data set and then propose MutaPredBERT, a prediction model fine-tuned on BioLinkBERT based on a question-answering formulation of the problem. We leverage transfer learning and use the help of large transformer-based models to achieve a Macro F1 score of >0.88 even with relatively small data for fine-tuning. Our work establishes the utility of large language models for the construction of structured sources of knowledge bases directly from scientific publications.

PMID:38110446 | DOI:10.1038/s41540-023-00324-2

ACS Synth Biol. 2023 Dec 18. doi: 10.1021/acssynbio.3c00606. Online ahead of print.

ABSTRACT

1,2-Butanediol (1,2-BDO) is an important platform chemical widely utilized in the synthesis of polyester polyols, plasticizers, cosmetics, and pharmaceuticals. However, no natural metabolic pathway for its biosynthesis has been identified, and biological production of 1,2-BDO from renewable bioresources has not been reported so far. In this study, we designed and experimentally verified a feasible non-natural synthesis pathway for the de novo production of 1,2-BDO from renewable carbohydrates for the first time. This pathway extends the l-threonine synthesis pathway by introducing two artificial metabolic modules to sequentially convert l-threonine into 2-hydroxybutyric acid and 1,2-BDO. Following key enzyme screening and enhancement of l-threonine synthesis module in the chassis microorganism, the best engineered Escherichia coli strain was able to produce 0.15 g/L 1,2-BDO using glucose as the sole carbon source. This work lays the foundation for the bioproduction of 1,2-BDO from renewable resources.

PMID:38110368 | DOI:10.1021/acssynbio.3c00606

Cell Death Dis. 2023 Dec 18;14(12):841. doi: 10.1038/s41419-023-06296-9.

ABSTRACT

Long non-coding RNAs (lncRNAs) comprise the most representative transcriptional units of the mammalian genome. They are associated with organ development linked with the emergence of cardiovascular diseases. We used bioinformatic approaches, machine learning algorithms, systems biology analyses, and statistical techniques to define co-expression modules linked to heart development and cardiovascular diseases. We also uncovered differentially expressed transcripts in subpopulations of cardiomyocytes. Finally, from this work, we were able to identify eight cardiac cell-types; several new coding, lncRNA, and pcRNA markers; two cardiomyocyte subpopulations at four different time points (ventricle E9.5, left ventricle E11.5, right ventricle E14.5 and left atrium P0) that harbored co-expressed gene modules enriched in mitochondrial, heart development and cardiovascular diseases. Our results evidence the role of particular lncRNAs in heart development and highlight the usage of co-expression modular approaches in the cell-type functional definition.

PMID:38110334 | DOI:10.1038/s41419-023-06296-9

Biomacromolecules. 2023 Dec 18. doi: 10.1021/acs.biomac.3c00968. Online ahead of print.

ABSTRACT

Protein hydrogels represent an important and growing biomaterial for a multitude of applications, including diagnostics and drug delivery. We have previously explored the ability to engineer the thermoresponsive supramolecular assembly of coiled-coil proteins into hydrogels with varying gelation properties, where we have defined important parameters in the coiled-coil hydrogel design. Using Rosetta energy scores and Poisson-Boltzmann electrostatic energies, we iterate a computational design strategy to predict the gelation of coiled-coil proteins while simultaneously exploring five new coiled-coil protein hydrogel sequences. Provided this library, we explore the impact of in silico energies on structure and gelation kinetics, where we also reveal a range of blue autofluorescence that enables hydrogel disassembly and recovery. As a result of this library, we identify the new coiled-coil hydrogel sequence, Q5, capable of gelation within 24 h at 4 °C, a more than 2-fold increase over that of our previous iteration Q2. The fast gelation time of Q5 enables the assessment of structural transition in real time using small-angle X-ray scattering (SAXS) that is correlated to coarse-grained and atomistic molecular dynamics simulations revealing the supramolecular assembling behavior of coiled-coils toward nanofiber assembly and gelation. This work represents the first system of hydrogels with predictable self-assembly, autofluorescent capability, and a molecular model of coiled-coil fiber formation.

PMID:38110299 | DOI:10.1021/acs.biomac.3c00968

Cold Spring Harb Perspect Biol. 2023 Dec 18:a041463. doi: 10.1101/cshperspect.a041463. Online ahead of print.

ABSTRACT

Homologous protein sequences are wonderfully diverse, indicating many possible evolutionary "solutions" to the encoding of function. Consequently, one can construct statistical models of protein sequence by analyzing amino acid frequency across a large multiple sequence alignment. A central premise is that covariance between amino acid positions reflects coevolution due to a shared functional or biophysical constraint. In this review, we describe the implementation and discuss the advantages, limitations, and recent progress on two coevolution-based modeling approaches: (1) Potts models of protein sequence (direct coupling analysis [DCA]-like), and (2) the statistical coupling analysis (SCA). Each approach detects interesting features of protein sequence and structure-the former emphasizes local physical contacts throughout the structure, while the latter identifies larger evolutionarily coupled networks of residues. Recent advances in large-scale gene synthesis and high-throughput functional selection now motivate additional work to benchmark model performance across quantitative function prediction and de novo design tasks.

PMID:38110247 | DOI:10.1101/cshperspect.a041463

Life Sci Alliance. 2023 Dec 18;7(3):e202302309. doi: 10.26508/lsa.202302309. Print 2024 Mar.

ABSTRACT

Hematopoietic stem and progenitor cells (HSPCs) are known to respond to acute inflammation; however, little is understood about the dynamics and heterogeneity of these stress responses in HSPCs. Here, we performed single-cell sequencing during the sensing, response, and recovery phases of the inflammatory response of HSPCs to treatment (a total of 10,046 cells from four time points spanning the first 72 h of response) with the pro-inflammatory cytokine IFNα to investigate the HSPCs' dynamic changes during acute inflammation. We developed the essential novel computational approaches to process and analyze the resulting single-cell time series dataset. This includes an unbiased cell type annotation and abundance analysis post inflammation, tools for identification of global and cell type-specific responding genes, and a semi-supervised linear regression approach for response pseudotime reconstruction. We discovered a variety of different gene responses of the HSPCs to the treatment. Interestingly, we were able to associate a global reduced myeloid differentiation program and a locally enhanced pyroptosis activity with reduced myeloid progenitor and differentiated cells after IFNα treatment. Altogether, the single-cell time series analyses have allowed us to unbiasedly study the heterogeneous and dynamic impact of IFNα on the HSPCs.

PMID:38110222 | DOI:10.26508/lsa.202302309

Annu Rev Biophys. 2023 Dec 18. doi: 10.1146/annurev-biophys-030722-021957. Online ahead of print.

ABSTRACT

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data. Expected final online publication date for the Annual Review of Biophysics, Volume 53 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

PMID:38109374 | DOI:10.1146/annurev-biophys-030722-021957

Small. 2023 Dec 18:e2306482. doi: 10.1002/smll.202306482. Online ahead of print.

ABSTRACT

Inflammatory bowel disease (IBD) has become a globally prevalent chronic disease with no causal therapeutic options. Targeted drug delivery systems with selectivity for inflamed areas in the gastrointestinal tract promise to reduce severe drug-related side effects. By creating three distinct nanostructures (vesicles, spherical, and wormlike micelles) from the same amphiphilic block copolymer poly(butyl acrylate)-block-poly(ethylene oxide) (PBA-b-PEO), the effect of nanoparticle shape on human mucosal penetration is systematically identified. An Ussing chamber technique is established to perform the ex vivo experiments on human colonic biopsies, demonstrating that the shape of polymeric nanostructures represents a rarely addressed key to tissue selectivity required for efficient IBD treatment. Wormlike micelles specifically enter inflamed mucosa from patients with IBD, but no significant uptake is observed in healthy tissue. Spheres (≈25 nm) and vesicles (≈120 nm) enter either both normal and inflamed tissue types or do not penetrate any tissue. According to quantitative image analysis, the wormlike nanoparticles localize mainly within immune cells, facilitating specific targeting, which is crucial for further increasing the efficacy of IBD treatment. These findings therefore demonstrate the untapped potential of wormlike nanoparticles not only to selectively target the inflamed human mucosa, but also to target key pro-inflammatory cells.

PMID:38109123 | DOI:10.1002/smll.202306482

Front Pharmacol. 2023 Dec 1;14:1321880. doi: 10.3389/fphar.2023.1321880. eCollection 2023.

ABSTRACT

Tanshinone IIA (Tan IIA) is a fat-soluble compound extracted from Salvia miltiorrhiza, which has a protective effect against atherosclerosis (AS). Tan IIA can inhibit oxidative stress and inflammatory damage of vascular endothelial cells (VECs) and improve endothelial cell dysfunction. Tan IIA also has a good protective effect on vascular smooth muscle cells (VSMCs). It can reduce vascular stenosis by inhibiting the proliferation and migration of vascular smooth muscle cells (VSMCs), and improve the stability of the fibrous cap of atherosclerotic plaque by inhibiting apoptosis and inflammation of VSMCs. In addition, Tan IIA inhibits the inflammatory response of macrophages and the formation of foam cells in atherosclerotic plaques. In summary, Tan IIA improves AS through a complex pathway. We propose to further study the specific molecular targets of Tan IIA using systems biology methods, so as to fundamentally elucidate the mechanism of Tan IIA. It is worth mentioning that there is a lack of high-quality evidence-based medical data on Tan IIA treatment of AS. We recommend that a randomized controlled clinical trial be conducted to evaluate the exact efficacy of Tan IIA in improving AS. Finally, sodium tanshinone IIA sulfonate (STS) can cause adverse drug reactions in some patients, which needs our attention.

PMID:38108067 | PMC:PMC10722201 | DOI:10.3389/fphar.2023.1321880

Front Endocrinol (Lausanne). 2023 Dec 1;14:1235614. doi: 10.3389/fendo.2023.1235614. eCollection 2023.

ABSTRACT

INTRODUCTION: Pluripotent stem cells can be generated from somatic cells by the Yamanaka factors Oct4, Sox2, Klf4 and c-Myc.

METHODS: Mouse embryonic fibroblasts (MEFs) were transduced with the Yamanaka factors and generation of induced pluripotent stem cells (iPSCs) was assessed by formation of alkaline phosphatase positive colonies, pluripotency gene expression and embryod bodies formation.

RESULTS: The thyroid hormone triiodothyronine (T3) enhances MEFs reprogramming. T3-induced iPSCs resemble embryonic stem cells in terms of the expression profile and DNA methylation pattern of pluripotency genes, and of their potential for embryod body formation and differentiation into the three major germ layers. T3 induces reprogramming even though it increases expression of the cyclin kinase inhibitors p21 and p27, which are known to oppose acquisition of pluripotency. The actions of T3 on reprogramming are mainly mediated by the thyroid hormone receptor beta and T3 can enhance iPSC generation in the absence of c-Myc. The hormone cannot replace Oct4 on reprogramming, but in the presence of T3 is possible to obtain iPSCs, although with low efficiency, without exogenous Klf4. Furthermore, depletion of the corepressor NCoR (or Nuclear Receptor Corepressor 1) reduces MEFs reprogramming in the absence of the hormone and strongly decreases iPSC generation by T3 and also by 9cis-retinoic acid, a well-known inducer of reprogramming. NCoR depletion also markedly antagonizes induction of pluripotency gene expression by both ligands.

CONCLUSIONS: Inclusion of T3 on reprogramming strategies has a potential use in enhancing the generation of functional iPSCs for studies of cell plasticity, disease and regenerative medicine.

PMID:38107517 | PMC:PMC10722291 | DOI:10.3389/fendo.2023.1235614

Microlife. 2023 Nov 3;4:uqad045. doi: 10.1093/femsml/uqad045. eCollection 2023.

ABSTRACT

On 9-13 July 2023, the 10th FEMS Congress took place in Hamburg, Germany. As part of this major event in European microbiology, the European Academy of Microbiology (EAM) organized two full sessions. One of these sessions aimed to highlight the research of four recently elected EAM fellows and saw presentations on bacterial group behaviours and development of resistance to antibiotics, as well as on new RNA viruses including bacteriophages and giant viruses of amoebae. The other session included five frontline environmental microbiologists who showcased real-world examples of how human activities have disrupted the balance in microbial ecosystems, not just to assess the current situation but also to explore fresh approaches for coping with external disturbances. Both sessions were very well attended, and no doubt helped to gain the EAM and its fellows more visibility.

PMID:38107236 | PMC:PMC10723851 | DOI:10.1093/femsml/uqad045

F1000Res. 2023 Dec 1;12:172. doi: 10.12688/f1000research.130126.3. eCollection 2023.

ABSTRACT

Moesin is a cytoskeletal adaptor protein, involved in the modification of the actin cytoskeleton, with relevance to Alzheimer's Disease. Well characterized anti-Moesin antibodies would benefit the scientific community. In this study, we have characterized ten Moesin commercial antibodies in Western Blot, immunoprecipitation, and immunofluorescence using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. These studies are part of a larger, collaborative initiative seeking to address antibody reproducibility by characterizing commercially available antibodies for human proteins and publishing the results openly as a resource for the scientific community. While use of antibodies and protocols vary between laboratories, we encourage readers to use this report as a guide to select the most appropriate antibodies for their specific needs.

PMID:38106655 | PMC:PMC10724652 | DOI:10.12688/f1000research.130126.3

Patterns (N Y). 2023 Dec 1;4(12):100889. doi: 10.1016/j.patter.2023.100889. eCollection 2023 Dec 8.

ABSTRACT

Coronavirus disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has had extensive economic, social, and public health impacts in the United States and around the world. To date, there have been more than 600 million reported infections worldwide with more than 6 million reported deaths. Retrospective analysis, which identified comorbidities, risk factors, and treatments, has underpinned the response. As the situation transitions to an endemic, retrospective analyses using electronic health records will be important to identify the long-term effects of COVID-19. However, these analyses can be complicated by incomplete records, which makes it difficult to differentiate visits where the patient had COVID-19. To address this issue, we trained a random Forest classifier to assign a probability of a patient having been diagnosed with COVID-19 during each visit. Using these probabilities, we found that higher COVID-19 probabilities were associated with a future diagnosis of myocardial infarction, urinary tract infection, acute renal failure, and type 2 diabetes.

PMID:38106616 | PMC:PMC10724367 | DOI:10.1016/j.patter.2023.100889

Patterns (N Y). 2023 Dec 1;4(12):100890. doi: 10.1016/j.patter.2023.100890. eCollection 2023 Dec 8.

ABSTRACT

Predictive pattern mining is an approach used to construct prediction models when the input is represented by structured data, such as sets, graphs, and sequences. The main idea behind predictive pattern mining is to build a prediction model by considering unified inconsistent notation sub-structures, such as subsets, subgraphs, and subsequences (referred to as patterns), present in the structured data as features of the model. The primary challenge in predictive pattern mining lies in the exponential growth of the number of patterns with the complexity of the structured data. In this study, we propose the safe pattern pruning method to address the explosion of pattern numbers in predictive pattern mining. We also discuss how it can be effectively employed throughout the entire model building process in practical data analysis. To demonstrate the effectiveness of the proposed method, we conduct numerical experiments on regression and classification problems involving sets, graphs, and sequences.

PMID:38106611 | PMC:PMC10724371 | DOI:10.1016/j.patter.2023.100890

Front Immunol. 2023 Dec 1;14:1271879. doi: 10.3389/fimmu.2023.1271879. eCollection 2023.

ABSTRACT

Innate lymphoid cells (ILCs), as the innate counterpart of CD4+ T helper (Th) cells, play crucial roles in maintaining tissue homeostasis. While the ILC subsets and their corresponding Th subsets demonstrate significant similarities in core programming related to effector function and regulatory mechanisms, their principal distinctions, given their innate and adaptive lymphocyte nature, remain largely unknown. In this study, we have employed an integrative analysis of 294 bulk RNA-sequencing results across all ILC and Th subsets, using scRNA-seq algorithms. Consequently, we identify two genesets that predominantly differentiate ILCs from Th cells, as well as three genesets that distinguish various immune responses. Furthermore, through chromatin accessibility analysis, we find that the ILC geneset tends to rely on specific transcriptional regulation at promoter regions compared with the Th geneset. Additionally, we observe that ILCs and Th cells are under differential transcriptional regulation. For example, ILCs are under stronger regulation by multiple transcription factors, including RORα, GATA3, and NF-κB. Otherwise, Th cells are under stronger regulation by AP-1. Thus, our findings suggest that, despite the acknowledged similarities in effector functions between ILC subsets and corresponding Th subsets, the underlying regulatory machineries still exhibit substantial distinctions. These insights provide a comprehensive understanding of the unique roles played by each cell type during immune responses.

PMID:38106414 | PMC:PMC10722145 | DOI:10.3389/fimmu.2023.1271879

Front Neurosci. 2023 Dec 1;17:1331375. doi: 10.3389/fnins.2023.1331375. eCollection 2023.

ABSTRACT

[This corrects the article DOI: 10.3389/fnins.2023.1086208.].

PMID:38105926 | PMC:PMC10722402 | DOI:10.3389/fnins.2023.1331375

Eur J Neurosci. 2023 Dec 17. doi: 10.1111/ejn.16225. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is associated with abnormal accumulations of hyperphosphorylated tau and amyloid-β proteins, resulting in unique patterns of atrophy in the brain. We aimed to elucidate some characteristics of the AD's morphometric networks constructed by associating different morphometric features among brain areas and evaluating their relationship to Mini-Mental State Examination total score and age. Three-dimensional T1-weighted (3DT1) image data scanned by the same 1.5T magnetic resonance imaging (MRI) were obtained from 62 AD patients and 41 healthy controls (HCs) and were analysed by using FreeSurfer. The associations of the extracted six morphometric features between regions were estimated by correlation coefficients. The global and local graph theoretical measures for this network were evaluated. Associations between graph theoretical measures and age, sex and cognition were evaluated by multiple regression analysis in each group. Global measures of integration: global efficiency and mean information centrality were significantly higher in AD patients. Local measures of integration: node global efficiency and information centrality were significantly higher in the entorhinal cortex, fusiform gyrus and posterior cingulate cortex of AD patients but only in the left hemisphere. All global measures were correlated with age in AD patients but not in HCs. The information centrality was associated with age in AD's broad brain regions. Our results showed that altered morphometric networks due to AD are left-hemisphere dominant, suggesting that AD pathogenesis has a left-right asymmetry. Ageing has a unique impact on the morphometric networks in AD patients. The information centrality is a sensitive graph theoretical measure to detect this association.

PMID:38105486 | DOI:10.1111/ejn.16225