Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems.

BACKGROUND: The Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems. RESULTS: BiSDL's effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers. CONCLUSIONS: BiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.

Integrated analysis of gut metabolome, microbiome, and exfoliome data in an equine model of intestinal injury.

BACKGROUND: The equine gastrointestinal (GI) microbiome has been described in the context of various diseases. The observed changes, however, have not been linked to host function and therefore it remains unclear how specific changes in the microbiome alter cellular and molecular pathways within the GI tract. Further, non-invasive techniques to examine the host gene expression profile of the GI mucosa have been described in horses but not evaluated in response to interventions. Therefore, the objectives of our study were to (1) profile gene expression and metabolomic changes in an equine model of non-steroidal anti-inflammatory drug (NSAID)-induced intestinal inflammation and (2) apply computational data integration methods to examine host-microbiota interactions. METHODS: Twenty horses were randomly assigned to 1 of 2 groups (n = 10): control (placebo paste) or NSAID (phenylbutazone 4.4 mg/kg orally once daily for 9 days). Fecal samples were collected on days 0 and 10 and analyzed with respect to microbiota (16S rDNA gene sequencing), metabolomic (untargeted metabolites), and host exfoliated cell transcriptomic (exfoliome) changes. Data were analyzed and integrated using a variety of computational techniques, and underlying regulatory mechanisms were inferred from features that were commonly identified by all computational approaches. RESULTS: Phenylbutazone induced alterations in the microbiota, metabolome, and host transcriptome. Data integration identified correlation of specific bacterial genera with expression of several genes and metabolites that were linked to oxidative stress. Concomitant microbiota and metabolite changes resulted in the initiation of endoplasmic reticulum stress and unfolded protein response within the intestinal mucosa. CONCLUSIONS: Results of integrative analysis identified an important role for oxidative stress, and subsequent cell signaling responses, in a large animal model of GI inflammation. The computational approaches for combining non-invasive platforms for unbiased assessment of host GI responses (e.g., exfoliomics) with metabolomic and microbiota changes have broad application for the field of gastroenterology. Video Abstract.

Preclinical evaluation and first-in-dog clinical trials of PBMC-expanded natural killer cells for adoptive immunotherapy in dogs with cancer.

BACKGROUND: Natural killer (NK) cells are cytotoxic cells capable of recognizing heterogeneous cancer targets without prior sensitization, making them promising prospects for use in cellular immunotherapy. Companion dogs develop spontaneous cancers in the context of an intact immune system, representing a valid cancer immunotherapy model. Previously, CD5 depletion of peripheral blood mononuclear cells (PBMCs) was used in dogs to isolate a CD5dim-expressing NK subset prior to co-culture with an irradiated feeder line, but this can limit the yield of the final NK product. This study aimed to assess NK activation, expansion, and preliminary clinical activity in first-in-dog clinical trials using a novel system with unmanipulated PBMCs to generate our NK cell product. METHODS: Starting populations of CD5-depleted cells and PBMCs from healthy beagle donors were co-cultured for 14 days, phenotype, cytotoxicity, and cytokine secretion were measured, and samples were sequenced using the 3'-Tag-RNA-Seq protocol. Co-cultured human PBMCs and NK-isolated cells were also sequenced for comparative analysis. In addition, two first-in-dog clinical trials were performed in dogs with melanoma and osteosarcoma using autologous and allogeneic NK cells, respectively, to establish safety and proof-of-concept of this manufacturing approach. RESULTS: Calculated cell counts, viability, killing, and cytokine secretion were equivalent or higher in expanded NK cells from canine PBMCs versus CD5-depleted cells, and immune phenotyping confirmed a CD3-NKp46+ product from PBMC-expanded cells at day 14. Transcriptomic analysis of expanded cell populations confirmed upregulation of NK activation genes and related pathways, and human NK cells using well-characterized NK markers closely mirrored canine gene expression patterns. Autologous and allogeneic PBMC-derived NK cells were successfully expanded for use in first-in-dog clinical trials, resulting in no serious adverse events and preliminary efficacy data. RNA sequencing of PBMCs from dogs receiving allogeneic NK transfer showed patient-unique gene signatures with NK gene expression trends in response to treatment. CONCLUSIONS: Overall, the use of unmanipulated PBMCs appears safe and potentially effective for canine NK immunotherapy with equivalent to superior results to CD5 depletion in NK expansion, activation, and cytotoxicity. Our preclinical and clinical data support further evaluation of this technique as a novel platform for optimizing NK immunotherapy in dogs.

Germline homozygosity and allelic imbalance of HLA-I are common in esophagogastric adenocarcinoma and impair the repertoire of immunogenic peptides.

BACKGROUND: The individual HLA-I genotype is associated with cancer, autoimmune diseases and infections. This study elucidates the role of germline homozygosity or allelic imbalance of HLA-I loci in esophago-gastric adenocarcinoma (EGA) and determines the resulting repertoires of potentially immunogenic peptides. METHODS: HLA genotypes and sequences of either (1) 10 relevant tumor-associated antigens (TAAs) or (2) patient-specific mutation-associated neoantigens (MANAs) were used to predict good-affinity binders using an in silico approach for MHC-binding (www.iedb.org). Imbalanced or lost expression of HLA-I-A/B/C alleles was analyzed by transcriptome sequencing. FluoroSpot assays and TCR sequencing were used to determine peptide-specific T-cell responses. RESULTS: We show that germline homozygosity of HLA-I genes is significantly enriched in EGA patients (n=80) compared with an HLA-matched reference cohort (n=7605). Whereas the overall mutational burden is similar, the repertoire of potentially immunogenic peptides derived from TAAs and MANAs was lower in homozygous patients. Promiscuity of peptides binding to different HLA-I molecules was low for most TAAs and MANAs and in silico modeling of the homozygous to a heterozygous HLA genotype revealed normalized peptide repertoires. Transcriptome sequencing showed imbalanced expression of HLA-I alleles in 75% of heterozygous patients. Out of these, 33% showed complete loss of heterozygosity, whereas 66% had altered expression of only one or two HLA-I molecules. In a FluoroSpot assay, we determined that peptide-specific T-cell responses against NY-ESO-1 are derived from multiple peptides, which often exclusively bind only one HLA-I allele. CONCLUSION: The high frequency of germline homozygosity in EGA patients suggests reduced cancer immunosurveillance leading to an increased cancer risk. Therapeutic targeting of allelic imbalance of HLA-I molecules should be considered in EGA.

No evidence that a transmissible cancer has shifted from emergence to endemism in Tasmanian devils.

Tasmanian devils are endangered by a transmissible cancer known as Tasmanian devil facial tumour 1 (DFT1). A 2020 study by Patton et al. (Science 370, eabb9772 (doi:10.1126/science.abb9772)) used genome data from DFT1 tumours to produce a dated phylogenetic tree for this transmissible cancer lineage, and thence, using phylodynamics models, to estimate its epidemiological parameters and predict its future trajectory. It concluded that the effective reproduction number for DFT1 had declined to a value of one, and that the disease had shifted from emergence to endemism. We show that the study is based on erroneous mutation calls and flawed methodology, and that its conclusions cannot be substantiated.

Integrating biological knowledge for mechanistic inference in the host-associated microbiome.

Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.

Haplotype function score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits.

We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS-trait associations with a significance of p < 5 × 10-8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway-trait associations and 153 tissue-trait associations with strong biological interpretability, including 'circadian pathway-chronotype' and 'arachidonic acid-intelligence'. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1-39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

Scattered throughout the human genome are variations in the genetic code that make individuals more or less likely to develop certain traits. To identify these variants, scientists carry out Genome-wide association studies (GWAS) which compare the DNA variants of large groups of people with and without the trait of interest. This method has been able to find the underlying genes for many human diseases, but it has limitations. For instance, some variations are linked together due to where they are positioned within DNA, which can result in GWAS falsely reporting associations between genetic variants and traits. This phenomenon, known as linkage equilibrium, can be avoided by analyzing functional genomics which looks at the multiple ways a gene's activity can be influenced by a variation. For instance, how the gene is copied and decoded in to proteins and RNA molecules, and the rate at which these products are generated. Researchers can now use an artificial intelligence technique called deep learning to generate functional genomic data from a particular DNA sequence. Here, Song et al. used one of these deep learning models to calculate the functional genomics of haplotypes, groups of genetic variants inherited from one parent. The approach was applied to DNA samples from over 350 thousand individuals included in the UK BioBank. An activity score, defined as the haplotype function score (or HFS for short), was calculated for at least two haplotypes per individual, and then compared to various complex traits like height or bone density. Song et al. found that the HFS framework was better at finding links between genes and specific traits than existing methods. It also provided more information on the biology that may be underpinning these outcomes. Although more work is needed to reduce the computer processing times required to calculate the HFS, Song et al. believe that their new method has the potential to improve the way researchers identify links between genes and human traits.

RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes.

RNA-binding proteins (RBPs) are central to key functions such as post-transcriptional regulation, mRNA stability, and adaptation to varied environmental conditions in prokaryotes. While the majority of research has concentrated on eukaryotic RBPs, recent developments underscore the crucial involvement of prokaryotic RBPs. Although computational methods have emerged in recent years to identify RBPs, they have fallen short in accurately identifying prokaryotic RBPs due to their generic nature. To bridge this gap, we introduce RBProkCNN, a novel machine learning-driven computational model meticulously designed for the accurate prediction of prokaryotic RBPs. The prediction process involves the utilization of eight shallow learning algorithms and four deep learning models, incorporating PSSM-based evolutionary features. By leveraging a convolutional neural network (CNN) and evolutionarily significant features selected through extreme gradient boosting variable importance measure, RBProkCNN achieved the highest accuracy in five-fold cross-validation, yielding 98.04% auROC and 98.19% auPRC. Furthermore, RBProkCNN demonstrated robust performance with an independent dataset, showcasing a commendable 95.77% auROC and 95.78% auPRC. Noteworthy is its superior predictive accuracy when compared to several state-of-the-art existing models. RBProkCNN is available as an online prediction tool (https://iasri-sg.icar.gov.in/rbprokcnn/), offering free access to interested users. This tool represents a substantial contribution, enriching the array of resources available for the accurate and efficient prediction of prokaryotic RBPs.

A logic-incorporated gene regulatory network deciphers principles in cell fate decisions.

Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.

Unveiling the signaling network of FLT3-ITD AML improves drug sensitivity prediction.

Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In the setting of acute myeloid leukemia (AML), patient-drug treatment matching fails in a subset of patients harboring atypical internal tandem duplications (ITDs) in the tyrosine kinase domain of the FLT3 gene. To address this unmet medical need, here we develop a systems-based strategy that integrates multiparametric analysis of crucial signaling pathways, and patient-specific genomic and transcriptomic data with a prior knowledge signaling network using a Boolean-based formalism. By this approach, we derive personalized predictive models describing the signaling landscape of AML FLT3-ITD positive cell lines and patients. These models enable us to derive mechanistic insight into drug resistance mechanisms and suggest novel opportunities for combinatorial treatments. Interestingly, our analysis reveals that the JNK kinase pathway plays a crucial role in the tyrosine kinase inhibitor response of FLT3-ITD cells through cell cycle regulation. Finally, our work shows that patient-specific logic models have the potential to inform precision medicine approaches.

Protein language model-embedded geometric graphs power inter-protein contact prediction.

Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions. Although significant improvement has been made in inter-protein contact prediction recently, there is still a large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets illustrates that PLMGraph-Inter outperforms five top inter-protein contact prediction methods, including DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter, by large margins. In addition, we also show that the prediction of PLMGraph-Inter can complement the result of AlphaFold-Multimer. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein-protein docking can dramatically improve its performance for protein complex structure prediction.

Dilated cardiomyopathy-associated skeletal muscle actin (ACTA1) mutation R256H disrupts actin structure and function and causes cardiomyocyte hypocontractility.

Skeletal muscle actin (ACTA1) mutations are a prevalent cause of skeletal myopathies consistent with ACTA1's high expression in skeletal muscle. Rare de novo mutations in ACTA1 associated with combined cardiac and skeletal myopathies have been reported, but ACTA1 represents only ~20% of the total actin pool in cardiomyocytes, making its role in cardiomyopathy controversial. Here we demonstrate how a mutation in an actin isoform expressed at low levels in cardiomyocytes can cause cardiomyopathy by focusing on a unique ACTA1 mutation, R256H. We previously identified this mutation in multiple family members with dilated cardiomyopathy (DCM), who had reduced systolic function without clinical skeletal myopathy. Using a battery of multiscale biophysical tools, we show that R256H has potent functional effects on ACTA1 function at the molecular scale and in human cardiomyocytes. Importantly, we demonstrate that R256H acts in a dominant manner, where the incorporation of small amounts of mutant protein into thin filaments is sufficient to disrupt molecular contractility, and that this effect is dependent on the presence of troponin and tropomyosin. To understand the structural basis of this change in regulation, we resolved a structure of R256H filaments using Cryo-EM, and we see alterations in actin's structure that have the potential to disrupt interactions with tropomyosin. Finally, we show that ACTA1R256H/+ human induced pluripotent stem cell cardiomyocytes demonstrate reduced contractility and sarcomeric disorganization. Taken together, we demonstrate that R256H has multiple effects on ACTA1 function that are sufficient to cause reduced contractility and establish a likely causative relationship between ACTA1 R256H and clinical cardiomyopathy.

Replication efficiencies of human cytomegalovirus-infected epithelial cells are dependent on source of virus production.

Human cytomegalovirus (HCMV) is a prevalent betaherpesvirus, and infection can lead to a range of symptomatology from mononucleosis to sepsis in immunocompromised individuals. HCMV is also the leading viral cause of congenital birth defects. Lytic replication is supported by many cell types with different kinetics and efficiencies leading to a plethora of pathologies. The goal of these studies was to elucidate HCMV replication efficiencies for viruses produced on different cell types upon infection of epithelial cells by combining experimental approaches with data-driven computational modeling. HCMV was generated from a common genetic background of TB40-BAC4, propagated on fibroblasts (TB40Fb) or epithelial cells (TB40Epi), and used to infect epithelial cells. We quantified cell-associated viral genomes (vDNA), protein levels (pUL44, pp28), and cell-free titers over time for each virus at different multiplicities of infection. We combined experimental quantification with data-driven simulations and determined that parameters describing vDNA synthesis were similar between sources. We found that pUL44 accumulation was higher in TB40Fb than TB40Epi. In contrast, pp28 accumulation was higher in TB40Epi which coincided with a significant increase in titer for TB40Epi over TB40Fb. These differences were most evident during live-cell imaging, which revealed syncytia-like formation during infection by TB40Epi. Simulations of the late lytic replication cycle yielded a larger synthesis constant for pp28 in TB40Epi along with increase in virus output despite similar rates of genome synthesis. By combining experimental and computational modeling approaches, our studies demonstrate that the cellular source of propagated virus impacts viral replication efficiency in target cell types.

Proteogenomic Gene Structure Validation in the Pineapple Genome.

MD2 pineapple (Ananas comosus) is the second most important tropical crop that preserves crassulacean acid metabolism (CAM), which has high water-use efficiency and is fast becoming the most consumed fresh fruit worldwide. Despite the significance of environmental efficiency and popularity, until very recently, its genome sequence has not been determined and a high-quality annotated proteome has not been available. Here, we have undertaken a pilot proteogenomic study, analyzing the proteome of MD2 pineapple leaves using liquid chromatography-mass spectrometry (LC-MS/MS), which validates 1781 predicted proteins in the annotated F153 (V3) genome. In addition, a further 603 peptide identifications are found that map exclusively to an independent MD2 transcriptome-derived database but are not found in the standard F153 (V3) annotated proteome. Peptide identifications derived from these MD2 transcripts are also cross-referenced to a more recent and complete MD2 genome annotation, resulting in 402 nonoverlapping peptides, which in turn support 30 high-quality gene candidates novel to both pineapple genomes. Many of the validated F153 (V3) genes are also supported by an independent proteomics data set collected for an ornamental pineapple variety. The contigs and peptides have been mapped to the current F153 genome build and are available as bed files to display a custom gene track on the Ensembl Plants region viewer. These analyses add to the knowledge of experimentally validated pineapple genes and demonstrate the utility of transcript-derived proteomics to discover both novel genes and genetic structure in a plant genome, adding value to its annotation.

Artificial Intelligence and Computational Biology in Gene Therapy: A Review.

One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.

Rescue of Mycobacterium bovis DNA Obtained from Cultured Samples during Official Surveillance of Animal TB: Key Steps for Robust Whole Genome Sequence Data Generation.

Epidemiological surveillance of animal tuberculosis (TB) based on whole genome sequencing (WGS) of Mycobacterium bovis has recently gained track due to its high resolution to identify infection sources, characterize the pathogen population structure, and facilitate contact tracing. However, the workflow from bacterial isolation to sequence data analysis has several technical challenges that may severely impact the power to understand the epidemiological scenario and inform outbreak response. While trying to use archived DNA from cultured samples obtained during routine official surveillance of animal TB in Portugal, we struggled against three major challenges: the low amount of M. bovis DNA obtained from routinely processed animal samples; the lack of purity of M. bovis DNA, i.e., high levels of contamination with DNA from other organisms; and the co-occurrence of more than one M. bovis strain per sample (within-host mixed infection). The loss of isolated genomes generates missed links in transmission chain reconstruction, hampering the biological and epidemiological interpretation of data as a whole. Upon identification of these challenges, we implemented an integrated solution framework based on whole genome amplification and a dedicated computational pipeline to minimize their effects and recover as many genomes as possible. With the approaches described herein, we were able to recover 62 out of 100 samples that would have otherwise been lost. Based on these results, we discuss adjustments that should be made in official and research laboratories to facilitate the sequential implementation of bacteriological culture, PCR, downstream genomics, and computational-based methods. All of this in a time frame supporting data-driven intervention.

Dysregulated microRNAs in prostate cancer: In silico prediction and in vitro validation.

Objectives: MicroRNAs, which are micro-coordinators of gene expression, have been recently investigated as a potential treatment for cancer. The study used computational techniques to identify microRNAs that could target a set of genes simultaneously. Due to their multi-target-directed nature, microRNAs have the potential to impact multiple key pathways and their pathogenic cross-talk. Materials and Methods: We identified microRNAs that target a prostate cancer-associated gene set using integrated bioinformatics analyses and experimental validation. The candidate gene set included genes targeted by clinically approved prostate cancer medications. We used STRING, GO, and KEGG web tools to confirm gene-gene interactions and their clinical significance. Then, we employed integrated predicted and validated bioinformatics approaches to retrieve hsa-miR-124-3p, 16-5p, and 27a-3p as the top three relevant microRNAs. KEGG and DIANA-miRPath showed the related pathways for the candidate genes and microRNAs. Results: The Real-time PCR results showed that miR-16-5p simultaneously down-regulated all genes significantly except for PIK3CA/CB in LNCaP; miR-27a-3p simultaneously down-regulated all genes significantly, excluding MET in LNCaP and PIK3CA in PC-3; and miR-124-3p could not down-regulate significantly PIK3CB, MET, and FGFR4 in LNCaP and FGFR4 in PC-3. Finally, we used a cell cycle assay to show significant G0/G1 arrest by transfecting miR-124-3p in LNCaP and miR-16-5p in both cell lines. Conclusion: Our findings suggest that this novel approach may have therapeutic benefits and these predicted microRNAs could effectively target the candidate genes.

Genome-scale annotation of protein binding sites via language model and geometric deep learning.

Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven't fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.

Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies.

Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.

Mir-155-5p targets TP53INP1 to promote proliferative phenotype in hypersensitivity pneumonitis lung fibroblasts.

Background: Hypersensitivity pneumonitis (HP) is an inflammatory disorder affecting lung parenchyma and often evolves into fibrosis (fHP). The altered regulation of genes involved in the pathogenesis of the disease is not well comprehended, while the role of microRNAs in lung fibroblasts remains unexplored. Methods: We used integrated bulk RNA-Seq and enrichment pathway bioinformatic analyses to identify differentially expressed (DE)-miRNAs and genes (DEGs) associated with HP lungs. In vitro, we evaluated the expression and potential role of miR-155-5p in the phenotype of fHP lung fibroblasts. Loss and gain assays were used to demonstrate the impact of miR-155-5p on fibroblast functions. In addition, mir-155-5p and its target TP53INP1 were analyzed after treatment with TGF-ß, IL-4, and IL-17A. Results: We found around 50 DEGs shared by several databases that differentiate HP from control and IPF lungs, constituting a unique HP lung transcriptional signature. Additionally, we reveal 18 DE-miRNAs that may regulate these DEGs. Among the candidates likely associated with HP pathogenesis was miR-155-5p. Our findings indicate that increased miR-155-5p in fHP fibroblasts coincides with reduced TP53INP1 expression, high proliferative capacity, and a lack of senescence markers compared to IPF fibroblasts. Induced overexpression of miR-155-5p in normal fibroblasts remarkably increases the proliferation rate and decreases TP53INP1 expression. Conversely, miR-155-5p inhibition reduces proliferation and increases senescence markers. TGF-ß, IL-4, and IL-17A stimulated miR-155-5p overexpression in HP lung fibroblasts. Conclusion: Our findings suggest a distinctive signature of 53 DEGs in HP, including CLDN18, EEF2, CXCL9, PLA2G2D, and ZNF683, as potential targets for future studies. Likewise, 18 miRNAs, including miR-155-5p, could be helpful to establish differences between these two pathologies. The overexpression of miR-155-5p and downregulation of TP53INP1 in fHP lung fibroblasts may be involved in his proliferative and profibrotic phenotype. These findings may help differentiate and characterize their pathogenic features and understand their role in the disease.