The Network of Cancer Genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens

A marker gene-based method for identifying the cell-type of origin from single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) experiments provide opportunities to peer into complex tissues at single-cell resolution. However, insightful biological interpretation of scRNA-seq data relies upon precise identification of cell types. The ability to identify the origin of a cell quickly and accurately will greatly improve downstream analyses. We present Sargent, a transformation-free, cluster-free, single-cell annotation algorithm for rapidly identifying the cell types of origin based on cell type-specific markers. We demonstrate Sargent's high accuracy by annotating simulated datasets. Further, we compare Sargent performance against expert-annotated scRNA-seq data from human organs including PBMC, heart, kidney, and lung. We demonstrate that Sargent retains both the flexibility and biological interpretability of cluster-based manual annotation. Additionally, the automation eliminates the labor intensive and potentially biased user annotation, producing robust, reproducible, and scalable outputs.•Sargent is a transformation-free, cluster-free, single-cell annotation algorithm for rapidly identifying the cell types of origin based on cell type-specific markers.•Sargent retains both the flexibility and biological interpretability of cluster-based manual annotation.•Automation eliminates the labor intensive and potentially biased user annotation, producing robust, reproducible, and scalable outputs.

Consequences and opportunities arising due to sparser single-cell RNA-seq datasets

With the number of cells measured in single-cell RNA sequencing (scRNA-seq) datasets increasing exponentially and concurrent increased sparsity due to more zero counts being measured for many genes, we demonstrate here that downstream analyses on binary-based gene expression give similar results as count-based analyses. Moreover, a binary representation scales up to ~ 50-fold more cells that can be analyzed using the same computational resources. We also highlight the possibilities provided by binarized scRNA-seq data. Development of specialized tools for bit-aware implementations of downstream analytical tasks will enable a more fine-grained resolution of biological heterogeneity.

Predictive and robust gene selection for spatial transcriptomics

A prominent trend in single-cell transcriptomics is providing spatial context alongside a characterization of each cell's molecular state. This typically requires targeting an a priori selection of genes, often covering less than 1% of the genome, and a key question is how to optimally determine the small gene panel. We address this challenge by introducing a flexible deep learning framework, PERSIST, to identify informative gene targets for spatial transcriptomics studies by leveraging reference scRNA-seq data. Using datasets spanning different brain regions, species, and scRNA-seq technologies, we show that PERSIST reliably identifies panels that provide more accurate prediction of the genome-wide expression profile, thereby capturing more information with fewer genes. PERSIST can be adapted to specific biological goals, and we demonstrate that PERSIST's binarization of gene expression levels enables models trained on scRNA-seq data to generalize with to spatial transcriptomics data, despite the complex shift between these technologies.

Risk factors and actionable molecular signatures in COVID-19-associated lung adenocarcinoma and lung squamous cell carcinoma patients

The molecular mechanism of COVID-19's pathogenic effect on lung cancer patients is yet unknown. In this study, we used differential gene expression pattern analysis to try to figure out the possible disease mechanism of COVID-19 and its associated risk factors in patients with the two most common types of non-small-cell lung cancer, lung adenocarcinoma and lung squamous cell carcinoma. We also used network-based approaches to identify potential diagnostic and molecular targets for COVID-19-infected lung cancer patients. Our study showed that lung cancer and COVID-19 patients share 36 genes that are expressed differently and in common. Most of these genes are expressed in lung tissues and are mostly involved in the pathogenesis of different respiratory tract diseases. Additionally, we also found that COVID-19 may affect the expression of several cancer-associated genes in lung cancer patients, such as the oncogenes JUN, TNC, and POU2AF1. Moreover, we also reported that COVID-19 may predispose lung cancer patients to other diseases like acute liver failure and respiratory distress syndrome. Also, our findings in concert with published literature suggest that molecular signatures like hsa-mir-93-5p, CCNB2, IRF1, CD163, and different immune cell-based approaches could help both diagnose and treat this group of patients. Overall, the scientific results of this research will aid in the formulation of suitable management strategies as well as the development of diagnostic and therapeutic methods for COVID-19-infected lung cancer patients.

MultiSimNeNc: A network representation learning-based module identification method by network embedding and clustering

Accurate identification of gene modules based on biological networks is an effective approach to understanding gene patterns of cancer from a module-level perspective. However, most graph clustering algorithms just consider low-order topological connectivity, which limits their accuracy in gene module identification. In this study, we propose a novel network-based method, MultiSimNeNc, to identify modules in various types of networks by integrating network representation learning (NRL) and clustering algorithms. In this method, we first obtain the multi-order similarity of the network using graph convolution (GC). Then, we aggregate the multi-order similarity to characterize the network structure and use non-negative matrix factorization (NMF) to achieve low-dimensional node characterization. Finally, we predict the number of modules based on the bayesian information criterion (BIC) and use the gaussian mixture model (GMM) to identify modules. To testify to the efficacy of MultiSimeNc in module identification, we apply this method to two types of biological networks and six benchmark networks, where the biological networks are constructed based on the fusion of multi-omics data from glioblastoma (GBM). The analysis shows that MultiSimNeNc outperforms several state-of-the-art module identification algorithms in identification accuracy, which is an effective method for understanding biomolecular mechanisms of pathogenesis from a module-level perspective.

Epigenetic clocks suggest accelerated aging in patients with isolated REM Sleep Behavior Disorder

Isolated REM Sleep Behavior Disorder (iRBD) is the strongest prodromal marker for α-synucleinopathies. Overt α-synucleinopathies and aging share several mechanisms, but this relationship has been poorly investigated in prodromal phases. Using DNA methylation-based epigenetic clocks, we measured biological aging in videopolysomnography confirmed iRBD patients, videopolysomnography-negative and population-based controls. We found that iRBDs tended to be epigenetically older than controls, suggesting that accelerated aging characterizes prodromal neurodegeneration.

Haplotype sequence collection of ABO blood group alleles by long-read sequencing reveals putative A1-diagnostic variants

In the era of blood group genomics, reference collections of complete and fully resolved blood group gene alleles have gained high importance. For most blood groups, however, such collections are currently lacking, as resolving full-length gene sequences as haplotypes (ie, separated maternal/paternal origin) remains exceedingly difficult with both Sanger and short-read next-generation sequencing. Using the latest third-generation long-read sequencing, we generated a collection of fully resolved sequences for all 6 main ABO allele groups: ABO∗A1/A2/B/O.01.01/O.01.02/O.02. We selected 77 samples from an ABO genotype data set (n = 25 200) of serologically typed Swiss blood donors. The entire ABO gene was amplified in 2 overlapping long-range polymerase chain reactions (covering ∼23.6 kb) and sequenced by long-read Oxford Nanopore sequencing. For quality validation, 2 samples per ABO group were resequenced using Illumina and Pacific Biosciences technology. All 154 full-length ABO sequences were resolved as haplotypes. We observed novel, distinct sequence patterns for each ABO group. Most genetic diversity was found between, not within, ABO groups. Phylogenetic tree and haplotype network analyses highlighted distinct clades of each ABO group. Strikingly, our data uncovered 4 genetic variants putatively specific for ABO∗A1, for which direct diagnostic targets are currently lacking. We validated A1-diagnostic potential using whole-genome data (n = 4872) of a multiethnic cohort. Overall, our sequencing strategy proved powerful for producing high-quality ABO haplotypes and holds promise for generating similar collections for other blood groups. The publicly available collection of 154 haplotypes will serve as a valuable resource for molecular analyses of ABO, as well as studies about the function and evolutionary history of ABO.

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer

One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R2) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R2) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.

A new genome assembly of an African weakly electric fish (Campylomormyrus compressirostris, Mormyridae) indicates rapid gene family evolution in Osteoglossomorpha

BACKGROUND

Teleost fishes comprise more than half of the vertebrate species. Within teleosts, most phylogenies consider the split between Osteoglossomorpha and Euteleosteomorpha/Otomorpha as basal, preceded only by the derivation of the most primitive group of teleosts, the Elopomorpha. While Osteoglossomorpha are generally species poor, the taxon contains the African weakly electric fish (Mormyroidei), which have radiated into numerous species. Within the mormyrids, the genus Campylomormyrus is mostly endemic to the Congo Basin. Campylomormyrus serves as a model to understand mechanisms of adaptive radiation and ecological speciation, especially with regard to its highly diverse species-specific electric organ discharges (EOD). Currently, there are few well-annotated genomes available for electric fish in general and mormyrids in particular. Our study aims at producing a high-quality genome assembly and to use this to examine genome evolution in relation to other teleosts. This will facilitate further understanding of the evolution of the osteoglossomorpha fish in general and of electric fish in particular.

RESULTS

A high-quality weakly electric fish (C. compressirostris) genome was produced from a single individual with a genome size of 862 Mb, consisting of 1,497 contigs with an N50 of 1,399 kb and a GC-content of 43.69%. Gene predictions identified 34,492 protein-coding genes, which is a higher number than in the two other available Osteoglossomorpha genomes of Paramormyrops kingsleyae and Scleropages formosus. A Computational Analysis of gene Family Evolution (CAFE5) comparing 33 teleost fish genomes suggests an overall faster gene family turnover rate in Osteoglossomorpha than in Otomorpha and Euteleosteomorpha. Moreover, the ratios of expanded/contracted gene family numbers in Osteoglossomorpha are significantly higher than in the other two taxa, except for species that had undergone an additional genome duplication (Cyprinus carpio and Oncorhynchus mykiss). As potassium channel proteins are hypothesized to play a key role in EOD diversity among species, we put a special focus on them, and manually curated 16 Kv1 genes. We identified a tandem duplication in the KCNA7a gene in the genome of C. compressirostris.

CONCLUSIONS

We present the fourth genome of an electric fish and the third well-annotated genome for Osteoglossomorpha, enabling us to compare gene family evolution among major teleost lineages. Osteoglossomorpha appear to exhibit rapid gene family evolution, with more gene family expansions than contractions. The curated Kv1 gene family showed seven gene clusters, which is more than in other analyzed fish genomes outside Osteoglossomorpha. The KCNA7a, encoding for a potassium channel central for EOD production and modulation, is tandemly duplicated which may related to the diverse EOD observed among Campylomormyrus species.

SWEET: a single-sample network inference method for deciphering individual features in disease

Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we propose a sample-specific-weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e. sample weights) with the differential network between perturbed and aggregate networks. For a group of samples, the genome-wide sample weights can be assessed without prior knowledge of intrinsic subpopulations to address the network edge number bias caused by sample size differences. Compared with the state-of-the-art SIN inference methods, the SWEET SINs in 16 cancers more likely fit the scale-free property, display higher overlap with the human interactomes and perform better in identifying three types of cancer-related genes. Moreover, integrating SWEET SINs with a network proximity measure facilitates characterizing individual features and therapy in diseases, such as somatic mutation, mut-driver and essential genes. Biological experiments further validated two candidate repurposable drugs, albendazole for head and neck squamous cell carcinoma (HNSCC) and lung adenocarcinoma (LUAD) and encorafenib for HNSCC. By applying SWEET, we also identified two possible LUAD subtypes that exhibit distinct clinical features and molecular mechanisms. Overall, the SWEET method complements current SIN inference and analysis methods and presents a view of biological systems at the network level to offer numerous clues for further investigation and clinical translation in network medicine and precision medicine.

A holobiont approach towards polysaccharide degradation by the highly compartmentalised gut system of the soil-feeding higher termite Labiotermes labralis

BACKGROUND

Termites are among the most successful insects on Earth and can feed on a broad range of organic matter at various stages of decomposition. The termite gut system is often referred to as a micro-reactor and is a complex structure consisting of several components. It includes the host, its gut microbiome and fungal gardens, in the case of fungi-growing higher termites. The digestive tract of soil-feeding higher termites is characterised by radial and axial gradients of physicochemical parameters (e.g. pH, O₂ and H₂ partial pressure), and also differs in the density and structure of residing microbial communities. Although soil-feeding termites account for 60% of the known termite species, their biomass degradation strategies are far less known compared to their wood-feeding counterparts.

RESULTS

In this work, we applied an integrative multi-omics approach for the first time at the holobiont level to study the highly compartmentalised gut system of the soil-feeding higher termite Labiotermes labralis. We relied on 16S rRNA gene community profiling, metagenomics and (meta)transcriptomics to uncover the distribution of functional roles, in particular those related to carbohydrate hydrolysis, across different gut compartments and among the members of the bacterial community and the host itself. We showed that the Labiotermes gut was dominated by members of the Firmicutes phylum, whose abundance gradually decreased towards the posterior segments of the hindgut, in favour of Bacteroidetes, Proteobacteria and Verrucomicrobia. Contrary to expectations, we observed that L. labralis gut microbes expressed a high diversity of carbohydrate active enzymes involved in cellulose and hemicelluloses degradation, making the soil-feeding termite gut a unique reservoir of lignocellulolytic enzymes with considerable biotechnological potential. We also evidenced that the host cellulases have different phylogenetic origins and structures, which is possibly translated into their different specificities towards cellulose. From an ecological perspective, we could speculate that the capacity to feed on distinct polymorphs of cellulose retained in soil might have enabled this termite species to widely colonise the different habitats of the Amazon basin.

CONCLUSIONS

Our study provides interesting insights into the distribution of the hydrolytic potential of the highly compartmentalised higher termite gut. The large number of expressed enzymes targeting the different lignocellulose components make the Labiotermes worker gut a relevant lignocellulose-valorising model to mimic by biomass conversion industries.

Recent progress in nucleic acid detection with CRISPR

Recent advances in CRISPR-based biotechnologies have greatly expanded our capabilities to repurpose CRISPR for the development of molecular diagnostic systems. The key attribute that allows CRISPR to be widely utilized is its programmable and highly specific nature. In this review, we first illustrate the principle of the class 2 CRISPR nucleases for molecular diagnostics which originates from their immunologic defence systems. Next, we present the CRISPR-based schemes in the application of diagnostics with amplification-assisted or amplification-free strategies. By highlighting some of the recent advances we interpret how general bioengineering methodologies can be integrated with CRISPR. Finally, we discuss the challenges and exciting prospects for future CRISPR-based biosensing development. We hope that this review will guide the reader to systematically learn the start-of-the-art development of CRISPR-mediated nucleic acid detection and understand how to apply the CRISPR nucleases with different design concepts to more general applications in diagnostics and beyond.

In-silico computational approaches to study microbiota impacts on diseases and pharmacotherapy

Microorganisms have been linked to a variety of critical human disease, thanks to advances in sequencing technology and microbiology. The growing recognition of human microbe-disease relationships provides crucial insights into the underlying disease process from the perspective of pathogens, which is extremely useful for pathogenesis research, early diagnosis, and precision medicine and therapy. Microbe-based analysis in terms of diseases and related drug discovery can predict new connections/mechanisms and provide new concepts. These phenomena have been studied via various in-silico computational approaches. This review aims to elaborate on the computational works conducted on the microbe-disease and microbe-drug topics, discuss the computational model approaches used for predicting associations and provide comprehensive information on the related databases. Finally, we discussed potential prospects and obstacles in this field of study, while also outlining some recommendations for further enhancing predictive capabilities.

CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity

MOTIVATION

Integration of growing single-cell RNA sequencing datasets helps better understand cellular identity and function. The major challenge for integration is removing batch effects while preserving biological heterogeneities. Advances in contrastive learning have inspired several contrastive learning-based batch correction methods. However, existing contrastive-learning-based methods exhibit noticeable ad hoc trade-off between batch mixing and preservation of cellular heterogeneities (mix-heterogeneity trade-off). Therefore, a deliberate mix-heterogeneity trade-off is expected to yield considerable improvements in scRNA-seq dataset integration.

RESULTS

We develop a novel contrastive learning-based batch correction framework, CIAIRE, which achieves superior mix-heterogeneity trade-off. The key contributions of CLAIRE are proposal of two complementary strategies: construction strategy and refinement strategy, to improve the appropriateness of positive pairs. Construction strategy dynamically generates positive pairs by augmenting inter-batch mutual nearest neighbors (MNN) with intra-batch k-nearest neighbors (KNN), which improves the coverage of positive pairs for the whole distribution of shared cell types between batches. Refinement strategy aims to automatically reduce the potential false positive pairs from the construction strategy, which resorts to the memory effect of deep neural networks. We demonstrate that CLAIRE possesses superior mix-heterogeneity trade-off over existing contrastive learning-based methods. Benchmark results on six real datasets also show that CLAIRE achieves the best integration performance against eight state-of-the-art methods. Finally, comprehensive experiments are conducted to validate the effectiveness of CLAIRE.

AVAILABILITY AND IMPLEMENTATION

The source code and data used in this study can be found in https://github.com/CSUBioGroup/CLAIRE-release.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Systematizing Microbial Bioplastic Production for Developing Sustainable Bioeconomy: Metabolic Nexus Modeling, Economic and Environmental Technologies Assessment

The excessive usage of non-renewable resources to produce plastic commodities has incongruously influenced the environment's health. Especially in the times of COVID-19, the need for plastic-based health products has increased predominantly. Given the rise in global warming and greenhouse gas emissions, the lifecycle of plastic has been established to contribute to it significantly. Bioplastics such as polyhydroxy alkanoates, polylactic acid, etc. derived from renewable energy origin have been a magnificent alternative to conventional plastics and reconnoitered exclusively for combating the environmental footprint of petrochemical plastic. However, the economically reasonable and environmentally friendly procedure of microbial bioplastic production has been a hard nut to crack due to less scouted and inefficient process optimization and downstream processing methodologies. Thereby, meticulous employment of computational tools such as genome-scale metabolic modeling and flux balance analysis has been practiced in recent times to understand the effect of genomic and environmental perturbations on the phenotype of the microorganism. In-silico results not only aid us in determining the biorefinery abilities of the model microorganism but also curb our reliance on equipment, raw materials, and capital investment for optimizing the best conditions. Additionally, to accomplish sustainable large-scale production of microbial bioplastic in a circular bioeconomy, extraction, and refinement of bioplastic needs to be investigated extensively by practicing techno-economic analysis and life cycle assessment. This review put forth state-of-the-art know-how on the proficiency of these computational techniques in laying the foundation of an efficient bioplastic manufacturing blueprint, chiefly focusing on microbial polyhydroxy alkanoates (PHA) production and its efficacy in outplacing fossil based plastic products.

Genome structure-based Juglandaceae phylogenies contradict alignment-based phylogenies and substitution rates vary with DNA repair genes

In lineages of allopolyploid origin, sets of homoeologous chromosomes may coexist that differ in gene content and syntenic structure. Presence or absence of genes and microsynteny along chromosomal blocks can serve to differentiate subgenomes and to infer phylogenies. We here apply genome-structural data to infer relationships in an ancient allopolyploid lineage, the walnut family (Juglandaceae), by using seven chromosome-level genomes, two of them newly assembled. Microsynteny and gene-content analyses yield identical topologies that place Platycarya with Engelhardia as did a 1980s morphological-cladistic study. DNA-alignment-based topologies here and in numerous earlier studies instead group Platycarya with Carya and Juglans, perhaps misled by past hybridization. All available data support a hybrid origin of Juglandaceae from extinct or unsampled progenitors nested within, or sister to, Myricaceae. Rhoiptelea chiliantha, sister to all other Juglandaceae, contains proportionally more DNA repair genes and appears to evolve at a rate 2.6- to 3.5-times slower than the remaining species.

Navigating the CoGe Online Software Suite for Polyploidy Research

The CoGe software suite at genomevolution.org hosts a number of tools that facilitate genomic research on plant and animal whole-genome multiplication-polyploidy. SynMap permits analysis and visualization of two-way syntenic dotplot alignments of genomes, includes many options and data/graphics download possibilities, and even permits three-genome synteny maps and interactive views. FractBias is a tool that operates within SynMap that permits calculation and graphic display of genome fragments (such as chromosomes) of one species mapped to another, displaying both blockwise homology depths and the extent of syntenic gene (syntelog) loss following polyploidy events. SynMap macrosynteny results can segue into the microsynteny tool GEvo, which provides genome-browser-like views of homologous genome blocks. CoGe FeatView allows call-up of given gene features already stored in the CoGe resource, and CoGeBlast permits searches for additional features that can be analyzed or downloaded further. Links from these tools can be fed into SynFind, which can find syntenic blocks surrounding a feature across multiple specified genomes while also simultaneously providing overall genome-wide syntenic depth calculations that can be interpreted to reflect polyploidy levels. Here, we describe basic use of these tools on the CoGe software suite.

Evolution of pathogenicity-associated genes in Rhizoctonia solani AG1-IA by genome duplication and transposon-mediated gene function alterations

BACKGROUND

Rhizoctonia solani is a polyphagous fungal pathogen that causes diseases in crops. The fungal strains are classified into anastomosis groups (AGs); however, genomic complexity, diversification into the AGs and the evolution of pathogenicity-associated genes remain poorly understood.

RESULTS

We report a recent whole-genome duplication and sequential segmental duplications in AG1-IA strains of R. solani. Transposable element (TE) clusters have caused loss of synteny in the duplicated blocks and introduced differential structural alterations in the functional domains of several pathogenicity-associated paralogous gene pairs. We demonstrate that the TE-mediated structural variations in a glycosyl hydrolase domain and a GMC oxidoreductase domain in two paralogous pairs affect the pathogenicity of R. solani. Furthermore, to investigate the association of TEs with the natural selection and evolution of pathogenicity, we sequenced the genomes of forty-two rice field isolates of R. solani AG1-IA. The genomic regions with high population mutation rates and with the lowest nucleotide diversity are enriched with TEs. Genetic diversity analysis predicted the genes that are most likely under diversifying and purifying selections. We present evidence that a smaller variant of a glucosamine phosphate N-acetyltransferase (GNAT) protein, predicted to be under purifying selection, and an LPMP_AA9 domain-containing protein, predicted to be under diversifying selection, are important for the successful pathogenesis of R. solani in rice as well as tomato.

CONCLUSIONS

Our study has unravelled whole-genome duplication, TE-mediated neofunctionalization of genes and evolution of pathogenicity traits in R. solani AG1-IA. The pathogenicity-associated genes identified during the study can serve as novel targets for disease control.

Localized heterochrony integrates overgrowth potential of oncogenic clones

Somatic mutations occur frequently and can arise during embryogenesis, resulting in the formation of a patchwork of mutant clones. Such mosaicism has been implicated in a broad range of developmental anomalies; however, their etiology is poorly understood. Patients carrying a common somatic oncogenic mutation in either PIK3CA or AKT1 can present with disproportionally large digits or limbs. How mutant clones, carrying an oncogenic mutation that often drives unchecked proliferation, can lead to controlled and coordinated overgrowth is unknown. We use zebrafish to explore the growth dynamics of oncogenic clones during development. Here, in a subset of clones, we observed a local increase in proportion of the fin skeleton closely resembling overgrowth phenotypes in patients. We unravel the cellular and developmental mechanisms of these overgrowths, and pinpoint the cell type and timing of clonal expansion. Coordinated overgrowth is associated with rapid clone expansion during early pre-chondrogenic phase of bone development, inducing a heterochronic shift that drives the change in bone size. Our study details how development integrates and translates growth potential of oncogenic clones, thereby shaping the phenotypic consequences of somatic mutations.

Addressing the pervasive scarcity of structural annotation in eukaryotic algae

Despite a continuous increase in algal genome sequencing, structural annotations of most algal genome assemblies remain unavailable. This pervasive scarcity of genome annotation has restricted rigorous investigation of these genomic resources and may have precipitated misleading biological interpretations. However, the annotation process for eukaryotic algal species is often challenging as genomic resources and transcriptomic evidence are not always available. To address this challenge, we benchmark the cutting-edge gene prediction methods that can be generalized for a broad range of non-model eukaryotes. Using the most accurate methods selected based on high-quality algal genomes, we predict structural annotations for 135 unannotated algal genomes. Using previously available genomic data pooled together with new data obtained in this study, we identified the core orthologous genes and the multi-gene phylogeny of eukaryotic algae, including of previously unexplored algal species. This study not only provides a benchmark for the use of structural annotation methods on a variety of non-model eukaryotes, but also compensates for missing data in the current spectrum of algal genomic resources. These results bring us one step closer to the full potential of eukaryotic algal genomics.

Patient-derived cell-based pharmacogenomic assessment to unveil underlying resistance mechanisms and novel therapeutics for advanced lung cancer

BACKGROUND

A pharmacogenomic platform using patient-derived cells (PDCs) was established to identify the underlying resistance mechanisms and tailored treatment for patients with advanced or refractory lung cancer.

METHODS

Drug sensitivity screening and multi-omics datasets were acquired from lung cancer PDCs (n = 102). Integrative analysis was performed to explore drug candidates according to genetic variants, gene expression, and clinical profiles.

RESULTS

PDCs had genomic characteristics resembled with those of solid lung cancer tissues. PDC molecular subtyping classified patients into four groups: (1) inflammatory, (2) epithelial-to-mesenchymal transition (EMT)-like, (3) stemness, and (4) epithelial growth factor receptor (EGFR)-dominant. EGFR mutations of the EMT-like subtype were associated with a reduced response to EGFR-tyrosine kinase inhibitor therapy. Moreover, although RB1/TP53 mutations were significantly enriched in small-cell lung cancer (SCLC) PDCs, they were also present in non-SCLC PDCs. In contrast to its effect in the cell lines, alpelisib (a PI3K-AKT inhibitor) significantly inhibited both RB1/TP53 expression and SCLC cell growth in our PDC model. Furthermore, cell cycle inhibitors could effectively target SCLC cells. Finally, the upregulation of transforming growth factor-β expression and the YAP/TAZ pathway was observed in osimertinib-resistant PDCs, predisposing them to the EMT-like subtype. Our platform selected XAV939 (a WNT-TNKS-β-catenin inhibitor) for the treatment of osimertinib-resistant PDCs. Using an in vitro model, we further demonstrated that acquisition of osimertinib resistance enhances invasive characteristics and EMT, upregulates the YAP/TAZ-AXL axis, and increases the sensitivity of cancer cells to XAV939.

CONCLUSIONS

Our PDC models recapitulated the molecular characteristics of lung cancer, and pharmacogenomics analysis provided plausible therapeutic candidates.

MME+ fibro-adipogenic progenitors are the dominant adipogenic population during fatty infiltration in human skeletal muscle

Fatty infiltration, the ectopic deposition of adipose tissue within skeletal muscle, is mediated via the adipogenic differentiation of fibro-adipogenic progenitors (FAPs). We used single-nuclei and single-cell RNA sequencing to characterize FAP heterogeneity in patients with fatty infiltration. We identified an MME⁺ FAP subpopulation which, based on ex vivo characterization as well as transplantation experiments, exhibits high adipogenic potential. MME⁺ FAPs are characterized by low activity of WNT, known to control adipogenic commitment, and are refractory to the inhibitory role of WNT activators. Using preclinical models for muscle damage versus fatty infiltration, we show that many MME⁺ FAPs undergo apoptosis during muscle regeneration and differentiate into adipocytes under pathological conditions, leading to a reduction in their abundance. Finally, we utilized the varying fat infiltration levels in human hip muscles and found less MME⁺ FAPs in fatty infiltrated human muscle. Altogether, we have identified the dominant adipogenic FAP subpopulation in skeletal muscle.

A chromosome-level genome assembly of Plantago ovata

Plantago ovata is cultivated for production of its seed husk (psyllium). When wet, the husk transforms into a mucilage with properties suitable for pharmaceutical industries, utilised in supplements for controlling blood cholesterol levels, and food industries for making gluten-free products. There has been limited success in improving husk quantity and quality through breeding approaches, partly due to the lack of a reference genome. Here we constructed the first chromosome-scale reference assembly of P. ovata using a combination of 5.98 million PacBio and 636.5 million Hi-C reads. We also used corrected PacBio reads to estimate genome size and transcripts to generate gene models. The final assembly covers ~ 500 Mb with 99.3% gene set completeness. A total of 97% of the sequences are anchored to four chromosomes with an N50 of ~ 128.87 Mb. The P. ovata genome contains 61.90% repeats, where 40.04% are long terminal repeats. We identified 41,820 protein-coding genes, 411 non-coding RNAs, 108 ribosomal RNAs, and 1295 transfer RNAs. This genome will provide a resource for plant breeding programs to, for example, reduce agronomic constraints such as seed shattering, increase psyllium yield and quality, and overcome crop disease susceptibility.

Targeting Immune-Fibroblast Crosstalk in Myocardial Infarction and Cardiac Fibrosis

Inflammation and tissue fibrosis co-exist and are causally linked to organ dysfunction. However, the molecular mechanisms driving immune-fibroblast crosstalk in human cardiac disease remains unexplored and there are currently no therapeutics to target fibrosis. Here, we performed multi-omic single-cell gene expression, epitope mapping, and chromatin accessibility profiling in 38 donors, acutely infarcted, and chronically failing human hearts. We identified a disease-associated fibroblast trajectory marked by cell surface expression of fibroblast activator protein (FAP), which diverged into distinct myofibroblasts and pro-fibrotic fibroblast populations, the latter resembling matrifibrocytes. Pro-fibrotic fibroblasts were transcriptionally similar to cancer associated fibroblasts and expressed high levels of collagens and periostin (POSTN), thymocyte differentiation antigen 1 (THY-1), and endothelin receptor A (EDNRA) predicted to be driven by a RUNX1 gene regulatory network. We assessed the applicability of experimental systems to model tissue fibrosis and demonstrated that 3 different in vivo mouse models of cardiac injury were superior compared to cultured human heart and dermal fibroblasts in recapitulating the human disease phenotype. Ligand-receptor analysis and spatial transcriptomics predicted that interactions between C-C chemokine receptor type 2 (CCR2) macrophages and fibroblasts mediated by interleukin 1 beta (IL-1β) signaling drove the emergence of pro-fibrotic fibroblasts within spatially defined niches. This concept was validated through in silico transcription factor perturbation and in vivo inhibition of IL-1β signaling in fibroblasts where we observed reduced pro-fibrotic fibroblasts, preferential differentiation of fibroblasts towards myofibroblasts, and reduced cardiac fibrosis. Herein, we show a subset of macrophages signal to fibroblasts via IL-1β and rewire their gene regulatory network and differentiation trajectory towards a pro-fibrotic fibroblast phenotype. These findings highlight the broader therapeutic potential of targeting inflammation to treat tissue fibrosis and restore organ function.

Urban monitoring of antimicrobial resistance during a COVID-19 surge through wastewater surveillance

During the early phase of the COVID-19 pandemic, infected patients presented with symptoms similar to bacterial pneumonias and were treated with antibiotics before confirmation of a bacterial or fungal co-infection. We reasoned that wastewater surveillance could reveal potential relationships between reduced antimicrobial stewardship, specifically misprescribing antibiotics to treat viral infections, and the occurrence of antimicrobial resistance (AMR) in an urban community. Here, we analyzed microbial communities and AMR profiles in sewage samples from a wastewater treatment plant (WWTP) and a community shelter in Las Vegas, Nevada during a COVID-19 surge in December 2020. Using a respiratory pathogen and AMR enrichment next-generation sequencing panel, we identified four major phyla in the wastewater, including Actinobacteria, Firmicutes, Bacteroidetes and Proteobacteria. Consistent with antibiotics that were reportedly used to treat COVID-19 infections (e.g., fluoroquinolones and beta-lactams), we also measured a significant spike in corresponding AMR genes in the wastewater samples. AMR genes associated with colistin resistance (mcr genes) were also identified exclusively at the WWTP, suggesting that multidrug resistant bacterial infections were being treated during this time. We next compared the Las Vegas sewage data to local 2018-2019 antibiograms, which are antimicrobial susceptibility profile reports about common clinical pathogens. Similar to the discovery of higher levels of beta-lactamase resistance genes in sewage during 2020, beta-lactam antibiotics accounted for 51 ± 3 % of reported antibiotics used in antimicrobial susceptibility tests of 2018-2019 clinical isolates. Our data highlight how wastewater-based epidemiology (WBE) can be leveraged to complement more traditional surveillance efforts by providing community-level data to help identify current and emerging AMR threats.

Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing

Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network's central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.

Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning

Lysine-specific histone demethylase 1 (LSD-1) is an epigenetic enzyme that oxidatively cleaves methyl groups from monomethyl and dimethyl Lys4 of histone H3 and is highly overexpressed in different types of cancer. Therefore, it has been widely recognized as a promising therapeutic target for cancer therapy. Towards this end, we employed various Computer Aided Drug Design (CADD) approaches including pharmacophore modelling and machine learning. Pharmacophores generated by structure-based (SB) (either crystallographic-based or docking-based) and ligand-based (LB) (either supervised or unsupervised) modelling methods were allowed to compete within the context of genetic algorithm/machine learning and were assessed by Shapley additive explanation values (SHAP) to end up with three successful pharmacophores that were used to screen the National Cancer Institute (NCI) database. Seventy-five NCI hits were tested for their LSD-1 inhibitory properties against neuroblastoma SH-SY5Y cells, pancreatic carcinoma Panc-1 cells, glioblastoma U-87 MG cells and in vitro enzymatic assay, culminating in 3 nanomolar LSD-1 inhibitors of novel chemotypes.

Dietary enrichment of resistant starches or fibers differentially alter the feline fecal microbiome and metabolite profile

BACKGROUND

Cats are strict carnivores but possess a complex gastrointestinal (GI) microbial community that actively ferments dietary substrates that are not digested and reach the colon. The GI microbiota responses to dietary inclusion of resistant starches versus fibers have not been tested in cats. Thus, our objective was to evaluate the effects of diets enriched in resistant starch or fibers on the fecal characteristics, microbiome, and metabolite profiles of cats. Twelve healthy adult domestic shorthair cats (age = 9.6 ± 4.0 year; body weight = 3.9 ± 1.0 kg) were used in a replicated 3 × 3 Latin square design to test diets that were enriched with: (1) resistant starch (ERS), (2) a fiber-prebiotic-probiotic blend (FPPB), or (3) a fiber-prebiotic-probiotic blend + immune-modulating ingredients (iFPPB). In each 28-day period, 22 days of diet adaptation was followed by fecal and blood sample collection. Fecal samples were used for shotgun metagenomic sequencing. In addition, fecal and blood metabolite measurements and white blood cell stimulation was performed to assess immune function.

RESULTS

A total of 1690 bacterial species were identified, with 259 species differing between fiber-rich and ERS treatments. In comparison with fiber-rich treatments that increased diversity and promoted Firmicutes and Bacteroidetes populations, resistant starch reduced microbial diversity and fecal pH, led to a bloom in Actinobacteria, and modified Kyoto Encyclopedia of Genes and Genomes orthology (KO) terms pertaining to starch and sucrose metabolism, fatty acid biosynthesis and metabolism, epithelial cell signaling, among others. Resistant starch also differentially modified fecal metabolite concentrations with relevance to GI and overall host health (increased butyrate; decreased propionate and protein catabolites - branched-chain fatty acids; phenols and indoles; ammonia) and reduced blood cholesterol, which correlated strongly with microbial taxa and KO terms, and allowed for a high predictive efficiency of diet groups by random forest analysis.

CONCLUSION

Even though domestic cats and other carnivores evolved by eating low-carbohydrate diets rich in protein and fat, our results demonstrate that the feline microbiome and metabolite profiles are highly responsive to dietary change and in directions that are predictable.

Tandem repeats ubiquitously flank and contribute to translation initiation sites

Background

While the evolutionary divergence of cis-regulatory sequences impacts translation initiation sites (TISs), the implication of tandem repeats (TRs) in TIS selection remains largely elusive. Here, we employed the TIS homology concept to study a possible link between TRs of all core lengths and repeats with TISs.

Methods

Human, as reference sequence, and 83 other species were selected, and data was extracted on the entire protein-coding genes (n = 1,611,368) and transcripts (n = 2,730,515) annotated for those species from Ensembl 102. Following TIS identification, two different weighing vectors were employed to assign TIS homology, and the co-occurrence pattern of TISs with the upstream flanking TRs was studied in the selected species. The results were assessed in 10-fold cross-validation.

Results

On average, every TIS was flanked by 1.19 TRs of various categories within its 120 bp upstream sequence, per species. We detected statistically significant enrichment of non-homologous human TISs co-occurring with human-specific TRs. On the contrary, homologous human TISs co-occurred significantly with non-human-specific TRs. 2991 human genes had at least one transcript, TIS of which was flanked by a human-specific TR. Text mining of a number of the identified genes, such as CACNA1A, EIF5AL1, FOXK1, GABRB2, MYH2, SLC6A8, and TTN, yielded predominant expression and functions in the human brain and/or skeletal muscle.

Conclusion

We conclude that TRs ubiquitously flank and contribute to TIS selection at the trans-species level. Future functional analyses, such as a combination of genome editing strategies and in vitro protein synthesis may be employed to further investigate the impact of TRs on TIS selection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01075-5.

Cis-regulatory mutations associate with transcriptional and post-transcriptional deregulation of gene regulatory programs in cancers

Most cancer alterations occur in the noncoding portion of the human genome, where regulatory regions control gene expression. The discovery of noncoding mutations altering the cells' regulatory programs has been limited to few examples with high recurrence or high functional impact. Here, we show that transcription factor binding sites (TFBSs) have similar mutation loads to those in protein-coding exons. By combining cancer somatic mutations in TFBSs and expression data for protein-coding and miRNA genes, we evaluate the combined effects of transcriptional and post-transcriptional alterations on the regulatory programs in cancers. The analysis of seven TCGA cohorts culminates with the identification of protein-coding and miRNA genes linked to mutations at TFBSs that are associated with a cascading trans-effect deregulation on the cells' regulatory programs. Our analyses of cis-regulatory mutations associated with miRNAs recurrently predict 12 mature miRNAs (derived from 7 precursors) associated with the deregulation of their target gene networks. The predictions are enriched for cancer-associated protein-coding and miRNA genes and highlight cis-regulatory mutations associated with the dysregulation of key pathways associated with carcinogenesis. By combining transcriptional and post-transcriptional regulation of gene expression, our method predicts cis-regulatory mutations related to the dysregulation of key gene regulatory networks in cancer patients.

Strategies to inhibit FGFR4 V550L-driven rhabdomyosarcoma

BACKGROUND

Rhabdomyosarcoma (RMS) is a paediatric cancer driven either by fusion proteins (e.g., PAX3-FOXO1) or by mutations in key signalling molecules (e.g., RAS or FGFR4). Despite the latter providing opportunities for precision medicine approaches in RMS, there are currently no such treatments implemented in the clinic.

METHODS

We evaluated biologic properties and targeting strategies for the FGFR4 V550L activating mutation in RMS559 cells, which have a high allelic fraction of this mutation and are oncogenically dependent on FGFR4 signalling. Signalling and trafficking of FGFR4 V550L were characterised by confocal microscopy and proteomics. Drug effects were determined by live-cell imaging, MTS assay, and in a mouse model.

RESULTS

Among recently developed FGFR4-specific inhibitors, FGF401 inhibited FGFR4 V550L-dependent signalling and cell proliferation at low nanomolar concentrations. Two other FGFR4 inhibitors, BLU9931 and H3B6527, lacked potent activity against FGFR4 V550L. Alternate targeting strategies were identified by RMS559 phosphoproteomic analyses, demonstrating that RAS/MAPK and PI3K/AKT are essential druggable pathways downstream of FGFR4 V550L. Furthermore, we found that FGFR4 V550L is HSP90-dependent, and HSP90 inhibitors efficiently impeded RMS559 proliferation. In a RMS559 mouse xenograft model, the pan-FGFR inhibitor, LY2874455, did not efficiently inhibit growth, whereas FGF401 potently abrogated growth.

CONCLUSIONS

Our results pave the way for precision medicine approaches against FGFR4 V550L-driven RMS.

Whole-exome sequencing of BRCA-negative breast cancer patients and case-control analyses identify variants associated with breast cancer susceptibility

BACKGROUND

For the majority of individuals with early-onset or familial breast cancer referred for genetic testing, the genetic basis of their familial breast cancer remains unexplained. To identify novel germline variants associated with breast cancer predisposition, whole-exome sequencing (WES) was performed.

METHODS

WES on 290 BRCA1/BRCA2-negative Singaporeans with early-onset breast cancer and/or a family history of breast cancer was done. Case-control analysis against the East-Asian subpopulation (EAS) from the Genome Aggregation Database (gnomAD) identified variants enriched in cases, which were further selected by occurrence in cancer gene databases. Variants were further evaluated in repeated case-control analyses using a second case cohort from the database of Genotypes and Phenotypes (dbGaP) comprising 466 early-onset breast cancer patients from the United States, and a Singapore SG10K_Health control cohort.

RESULTS

Forty-nine breast cancer-associated germline pathogenic variants in 37 genes were identified in Singapore cases versus gnomAD (EAS). Compared against SG10K_Health controls, 13 of 49 variants remain significantly enriched (False Discovery Rate (FDR)-adjusted p < 0.05). Comparing these 49 variants in dbGaP cases against gnomAD (EAS) and SG10K_Health controls revealed 23 concordant variants that were significantly enriched (FDR-adjusted p < 0.05). Fourteen variants were consistently enriched in breast cancer cases across all comparisons (FDR-adjusted p < 0.05). Seven variants in GPRIN2, NRG1, MYO5A, CLIP1, CUX1, GNAS and MGA were confirmed by Sanger sequencing.

CONCLUSIONS

In conclusion, we have identified pathogenic variants in genes associated with breast cancer predisposition. Importantly, many of these variants were significant in a second case cohort from dbGaP, suggesting that the strategy of using case-control analysis to select variants could potentially be utilized for identifying variants associated with cancer susceptibility.

Investigating the impact of database choice on the accuracy of metagenomic read classification for the rumen microbiome

Microbiome analysis is quickly moving towards high-throughput methods such as metagenomic sequencing. Accurate taxonomic classification of metagenomic data relies on reference sequence databases, and their associated taxonomy. However, for understudied environments such as the rumen microbiome many sequences will be derived from novel or uncultured microbes that are not present in reference databases. As a result, taxonomic classification of metagenomic data from understudied environments may be inaccurate. To assess the accuracy of taxonomic read classification, this study classified metagenomic data that had been simulated from cultured rumen microbial genomes from the Hungate collection. To assess the impact of reference databases on the accuracy of taxonomic classification, the data was classified with Kraken 2 using several reference databases. We found that the choice and composition of reference database significantly impacted on taxonomic classification results, and accuracy. In particular, NCBI RefSeq proved to be a poor choice of database. Our results indicate that inaccurate read classification is likely to be a significant problem, affecting all studies that use insufficient reference databases. We observed that adding cultured reference genomes from the rumen to the reference database greatly improved classification rate and accuracy. We also demonstrated that metagenome-assembled genomes (MAGs) have the potential to further enhance classification accuracy by representing uncultivated microbes, sequences of which would otherwise be unclassified or incorrectly classified. However, classification accuracy was strongly dependent on the taxonomic labels assigned to these MAGs. We therefore highlight the importance of accurate reference taxonomic information and suggest that, with formal taxonomic lineages, MAGs have the potential to improve classification rate and accuracy, particularly in environments such as the rumen that are understudied or contain many novel genomes.

Deciphering the impact of genetic variation on human polyadenylation using APARENT2

BACKGROUND

3'-end processing by cleavage and polyadenylation is an important and finely tuned regulatory process during mRNA maturation. Numerous genetic variants are known to cause or contribute to human disorders by disrupting the cis-regulatory code of polyadenylation signals. Yet, due to the complexity of this code, variant interpretation remains challenging.

RESULTS

We introduce a residual neural network model, APARENT2, that can infer 3'-cleavage and polyadenylation from DNA sequence more accurately than any previous model. This model generalizes to the case of alternative polyadenylation (APA) for a variable number of polyadenylation signals. We demonstrate APARENT2's performance on several variant datasets, including functional reporter data and human 3' aQTLs from GTEx. We apply neural network interpretation methods to gain insights into disrupted or protective higher-order features of polyadenylation. We fine-tune APARENT2 on human tissue-resolved transcriptomic data to elucidate tissue-specific variant effects. By combining APARENT2 with models of mRNA stability, we extend aQTL effect size predictions to the entire 3' untranslated region. Finally, we perform in silico saturation mutagenesis of all human polyadenylation signals and compare the predicted effects of [Formula: see text] million variants against gnomAD. While loss-of-function variants were generally selected against, we also find specific clinical conditions linked to gain-of-function mutations. For example, we detect an association between gain-of-function mutations in the 3'-end and autism spectrum disorder. To experimentally validate APARENT2's predictions, we assayed clinically relevant variants in multiple cell lines, including microglia-derived cells.

CONCLUSIONS

A sequence-to-function model based on deep residual learning enables accurate functional interpretation of genetic variants in polyadenylation signals and, when coupled with large human variation databases, elucidates the link between functional 3'-end mutations and human health.

Single-cell RNA sequencing reveals rebalancing of immunological response in patients with periodontitis after non-surgical periodontal therapy

Background

Periodontitis is a major inflammatory disease of the oral mucosa that is not limited to the oral cavity but also has systemic consequences. Although the importance of chronic periodontitis has been emphasized, the systemic immune response induced by periodontitis and its therapeutic effects remain elusive. Here, we report the transcriptomes of peripheral blood mononuclear cells (PBMCs) from patients with periodontitis.

Methods

Using single-cell RNA sequencing, we profiled PBMCs from healthy controls and paired pre- and post-treatment patients with periodontitis. We extracted differentially expressed genes and biological pathways for each cell type and calculated activity scores reflecting cellular characteristics. Intercellular crosstalk was classified into therapy-responsive and -nonresponsive pathways.

Results

We analyzed pan-cellular differentially expressed genes caused by periodontitis and found that most cell types showed a significant increase in CRIP1, which was further supported by the increased levels of plasma CRIP1 observed in patients with periodontitis. In addition, activated cell type-specific ligand-receptor interactions, including the BTLA, IFN-γ, and RESISTIN pathways, were prominent in patients with periodontitis. Both the BTLA and IFN-γ pathways returned to similar levels in healthy controls after periodontal therapy, whereas the RESISTIN pathway was still activated even after therapy.

Conclusion

These data collectively provide insights into the transcriptome changes and molecular interactions that are responsive to periodontal treatment. We identified periodontitis-specific systemic inflammatory indicators and suggest unresolved signals of non-surgical therapy as future therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03702-2.

Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning

Cancer is one of the leading causes of death worldwide and can be caused by environmental aspects (for example, exposure to asbestos), by human behavior (such as smoking), or by genetic factors. To understand which genes might be involved in patients’ survival, researchers have invented prognostic genetic signatures: lists of genes that can be used in scientific analyses to predict if a patient will survive or not. In this study, we joined together five different prognostic signatures, each of them related to a specific cancer type, to generate a unique pan-cancer prognostic signature, that contains 207 unique probesets related to 187 unique gene symbols, with one particular probeset present in two cancer type-specific signatures (203072_at related to the MYO1E gene). We applied our proposed pan-cancer signature with the Random Forests machine learning method to 57 microarray gene expression datasets of 12 different cancer types, and analyzed the results. We also compared the performance of our pan-cancer signature with the performances of two alternative prognostic signatures, and with the performances of each cancer type-specific signature on their corresponding cancer type-specific datasets. Our results confirmed the effectiveness of our prognostic pan-cancer signature. Moreover, we performed a pathway enrichment analysis, which indicated an association between the signature genes and a protein-protein interaction analysis, that highlighted PIK3R2 and FN1 as key genes having a fundamental relevance in our signature, suggesting an important role in pan-cancer prognosis for both of them.

Epigenetics and ADHD: Reflections on Current Knowledge, Research Priorities and Translational Potential

Attention-deficit/hyperactivity disorder (ADHD) is a common and debilitating neurodevelopmental disorder influenced by both genetic and environmental factors, typically identified in the school-age years but hypothesized to have developmental origins beginning in utero. To improve current strategies for prediction, prevention and treatment, a central challenge is to delineate how, at a molecular level, genetic and environmental influences jointly shape ADHD risk, phenotypic presentation, and developmental course. Epigenetic processes that regulate gene expression, such as DNA methylation, have emerged as a promising molecular system in the search for both biomarkers and mechanisms to address this challenge. In this Current Opinion, we discuss the relevance of epigenetics (specifically DNA methylation) for ADHD research and clinical practice, starting with the current state of knowledge, what challenges we have yet to overcome, and what the future may hold in terms of methylation-based applications for personalized medicine in ADHD. We conclude that the field of epigenetics and ADHD is promising but is still in its infancy, and the potential for transformative translational applications remains a distant goal. Nevertheless, rapid methodological advances, together with the rise of collaborative science and increased availability of high-quality, longitudinal data make this a thriving research area that in future may contribute to the development of new tools for improved prediction, management, and treatment of ADHD.

Epidemiological and molecular description of nosocomial outbreak of COVID-19 Alpha (B.1.1.7) variant in Saudi Arabia

BACKGROUND

Nosocomial outbreaks frequently occurred during the Coronavirus disease 2019 (COVID-19) pandemic; however, sharing experiences on outbreak containment is vital to reduce the related burden in different locations.

OBJECTIVES

This article aims at sharing a practical experience on COVID-19 outbreak containment, including contact tracing, screening of target population, testing including molecular analysis, and preventive modalities. It also provides an epidemiological and molecular analysis of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‑CoV‑2) infection outbreak in a tertiary care hospital in Saudi Arabia.

METHODS

The outbreak occurred in a non-COVID medical ward at a tertiary care hospital in Jeddah, Saudi Arabia, from 22nd March and 15th April 2021. The multidisciplinary outbreak response team performed clinical and epidemiological investigations. Whole-Genome Sequencing (WGS) was implemented on selected isolates for further molecular characterization.

RESULTS

A total of eight nurses (20 % of the assigned ward nurses) and six patients (16.2 % of the ward admitted patients at the time of the outbreak) tested positive for the SARS-CoV-2 virus based on PCR testing. The outbreak investigation identified strong evidence of an epidemiologic link between the affected cases. WGS revealed a set of spike mutations and deletions specific to the Alpha variant (B.1.1.7 lineage). All the nurses had mild symptoms, and the fatality among the patients was 50 % (three out of the six patients).

CONCLUSIONS

The current nosocomial COVID-19 outbreak, caused by the Alpha variant, revealed multiple breaches in the adherence to the hospital infection control recommended measures. Containment strategies were successful in controlling the outbreak and limiting infection spread. Molecular analysis and genome sequencing are essential tools besides epidemiological investigation to inform appropriate actions, especially with emerging pathogens.

Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells

Background

Sophisticated methods to properly pre-process and analyze the increasing collection of single-cell RNA sequencing (scRNA-seq) data are increasingly being developed. On the contrary, the best practices to integrate these data into metabolic networks, aiming at describing metabolic phenotypes within a heterogeneous cell population, have been poorly investigated. In this regard, a critical factor is the presence of false zero values in reactions essential for a fundamental metabolic function, such as biomass or energy production. Here, we investigate the role of denoising strategies in mitigating this problem.

Methods

We applied state-of-the-art denoising strategies - namely MAGIC, ENHANCE, and SAVER - on three public scRNA-seq datasets. We then associated a metabolic flux distribution with every single cell by embedding its noise-free transcriptomics profile in the constraints of the optimization of a core metabolic model. Finally, we used the obtained single-cell optimal metabolic fluxes as features for cluster analysis. We compared the results obtained with different techniques, and with or without the use of denoising. We also investigated the possibility of applying denoising directly on the Reaction Activity Scores, which are metabolic features extracted from the read counts, rather than on the read counts.

Results

We show that denoising of transcriptomics data improves the clustering of single cells. We also illustrate that denoising restores important metabolic properties, such as the correlation between cell cycle phase and biomass accumulation, and between the RAS scores of reactions belonging to the same metabolic pathway. We show that MAGIC performs better than ENHANCE and SAVER, and that, denoising applied directly on the RAS matrix could be an effective alternative in removing false zero values from essential metabolic reactions.

Conclusions

Our results indicate that including denoising as a pre-processing operation represents a milestone to integrate scRNA-seq data into Flux Balance Analysis simulations and to perform single-cell cluster analysis with a focus on metabolic phenotypes.

The pangenome of the wheat pathogen Pyrenophora tritici-repentis reveals novel transposons associated with necrotrophic effectors ToxA and ToxB

BACKGROUND

In fungal plant pathogens, genome rearrangements followed by selection pressure for adaptive traits have facilitated the co-evolutionary arms race between hosts and their pathogens. Pyrenophora tritici-repentis (Ptr) has emerged recently as a foliar pathogen of wheat worldwide and its populations consist of isolates that vary in their ability to produce combinations of different necrotrophic effectors. These effectors play vital roles in disease development. Here, we sequenced the genomes of a global collection (40 isolates) of Ptr to gain insights into its gene content and genome rearrangements.

RESULTS

A comparative genome analysis revealed an open pangenome, with an abundance of accessory genes (~ 57%) reflecting Ptr's adaptability. A clear distinction between pathogenic and non-pathogenic genomes was observed in size, gene content, and phylogenetic relatedness. Chromosomal rearrangements and structural organization, specifically around effector coding genes, were detailed using long-read assemblies (PacBio RS II) generated in this work in addition to previously assembled genomes. We also discovered the involvement of large mobile elements associated with Ptr's effectors: ToxA, the gene encoding for the necrosis effector, was found as a single copy within a 143-kb 'Starship' transposon (dubbed 'Horizon') with a clearly defined target site and target site duplications. 'Horizon' was located on different chromosomes in different isolates, indicating mobility, and the previously described ToxhAT transposon (responsible for horizontal transfer of ToxA) was nested within this newly identified Starship. Additionally, ToxB, the gene encoding the chlorosis effector, was clustered as three copies on a 294-kb element, which is likely a different putative 'Starship' (dubbed 'Icarus') in a ToxB-producing isolate. ToxB and its putative transposon were missing from the ToxB non-coding reference isolate, but the homolog toxb and 'Icarus' were both present in a different non-coding isolate. This suggests that ToxB may have been mobile at some point during the evolution of the Ptr genome which is contradictory to the current assumption of ToxB vertical inheritance. Finally, the genome architecture of Ptr was defined as 'one-compartment' based on calculated gene distances and evolutionary rates.

CONCLUSIONS

These findings together reflect on the highly plastic nature of the Ptr genome which has likely helped to drive its worldwide adaptation and has illuminated the involvement of giant transposons in facilitating the evolution of virulence in Ptr.

Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma

Background

Single-cell technologies have enabled extensive analysis of complex immune composition, phenotype and interactions within tumor, which is crucial in understanding the mechanisms behind cancer progression and treatment resistance. Unfortunately, knowledge on cell phenotypes and their spatial interactions has only had limited impact on the pathological stratification of patients in the clinic so far. We explore the relationship between different tumor environments (TMEs) and response to immunotherapy by deciphering the composition and spatial relationships of different cell types.

Methods

Here we used imaging mass cytometry to simultaneously quantify 35 proteins in a spatially resolved manner on tumor tissues from 26 melanoma patients receiving anti-programmed cell death-1 (anti-PD-1) therapy. Using unsupervised clustering, we profiled 662,266 single cells to identify lymphocytes, myeloid derived monocytes, stromal and tumor cells, and characterized TME of different melanomas.

Results

Combined single-cell and spatial analysis reveals highly dynamic TMEs that are characterized with variable tumor and immune cell phenotypes and their spatial organizations in melanomas, and many of these multicellular features are associated with response to anti-PD-1 therapy. We further identify six distinct TME archetypes based on their multicellular compositions, and find that patients with different TME archetypes responded differently to anti-PD-1 therapy. Finally, we find that classifying patients based on the gene expression signature derived from TME archetypes predicts anti-PD-1 therapy response across multiple validation cohorts.

Conclusions

Our results demonstrate the utility of multiplex proteomic imaging technologies in studying complex molecular events in a spatially resolved manner for the development of new strategies for patient stratification and treatment outcome prediction.

Topoisomerase I poison-triggered immune gene activation is markedly reduced in human small-cell lung cancers by impairment of the cGAS/STING pathway

BACKGROUND

Current immunotherapy strategies have contrasting clinical results in human lung cancer patients as small-cell lung cancers (SCLC) often show features of immunological cold tumours. Topoisomerase 1 (TOP1) poisons are effective antitumor drugs with good efficacy against lung cancers.

METHODS

We used molecular, genetic and bioinformatic approaches to determine the mechanism of micronuclei formation induced by two TOP1 poisons in different human cancer cells, including SCLC cell lines.

RESULTS

TOP1 poisons stimulate similar levels of micronuclei in all tested cell lines but downstream effects can vary markedly. TOP1 poisons increase micronuclei levels with a mechanism involving R-loops as overexpression of RNaseH1 markedly reduces or abolishes both H2AX phosphorylation and micronuclei formation. TOP1 poison-induced micronuclei activate the cGAS/STING pathway leading to increased expression of immune genes in HeLa cells, but not in human SCLC cell lines, mainly due to lack of STING and/or cGAS expression. Moreover, the expression of STING and antigen-presenting machinery genes is generally downregulated in patient tumours of human lung cancer datasets.

CONCLUSIONS

Altogether, our data reveal an immune signalling mechanism activated by TOP1 poisons, which is often impaired in human SCLC tumours.

DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data

Identification of cancer driver genes plays an important role in precision oncology research, which is helpful to understand cancer initiation and progression. However, most existing computational methods mainly used the protein-protein interaction (PPI) networks, or treated the directed gene regulatory networks (GRNs) as the undirected gene-gene association networks to identify the cancer driver genes, which will lose the unique structure regulatory information in the directed GRNs, and then affect the outcome of the cancer driver gene identification. Here, based on the multi-omics pan-cancer data (i.e., gene expression, mutation, copy number variation, and DNA methylation), we propose a novel method (called DGMP) to identify cancer driver genes by jointing directed graph convolutional network (DGCN) and multilayer perceptron (MLP). DGMP learns the multi-omics features of genes as well as the topological structure features in GRN with the DGCN model and uses MLP to weigh more on gene features for mitigating the bias toward the graph topological features in the DGCN learning process. The results on three GRNs show that DGMP outperforms other existing state-of-the-art methods. The ablation experimental results on the DawnNet network indicate that introducing MLP into DGCN can offset the performance degradation of DGCN, and jointing MLP and DGCN can effectively improve the performance of identifying cancer driver genes. DGMP can identify not only the highly mutated cancer driver genes but also the driver genes harboring other kinds of alterations (e.g., differential expression and aberrant DNA methylation) or genes involved in GRNs with other cancer genes. The source code of DGMP can be freely downloaded from https://github.com/NWPU-903PR/DGMP.

A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery

Cancer has been constantly evolving and so is the research pertaining to cancer diagnosis and therapeutic regimens. Early detection and specific therapeutics are the key features of modern cancer therapy. These requirements can only be fulfilled with the integration of diverse high-throughput technologies. Integration of advanced omics methodology involving genomics, epigenomics, proteomics, and transcriptomics provide a clear understanding of multi-faceted cancer. In the past few years, tremendous high-throughput data have been generated from cancer genomics and epigenomic analyses, which on further methodological analyses can yield better biological insights. The major epigenetic alterations reported in cancer are DNA methylation levels, histone post-translational modifications, and epi-miRNA regulating the oncogenes and tumor suppressor genes. While the genomic analyses like gene expression profiling, cancer gene prediction, and genome annotation divulge the genetic alterations in oncogenes or tumor suppressor genes. Also, systems biology approach using biological networks is being extensively used to identify novel cancer biomarkers. Therefore, integration of these multi-dimensional approaches will help to identify potential diagnostic and therapeutic biomarkers. Here, we reviewed the critical databases and tools dedicated to various epigenomic and genomic alterations in cancer. The review further focuses on the multi-omics resources available for further validating the identified cancer biomarkers. We also highlighted the tools for cancer biomarker discovery using a systems biology approach utilizing genomic and epigenomic data. Biomarkers predicted using such integrative approaches are shown to be more clinically relevant.

DrDimont: explainable drug response prediction from differential analysis of multi-omics networks

MOTIVATION

While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem.

RESULTS

We present DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e. molecular differences that are the source of high differential drug scores can be retrieved. We predict differential drug response in breast cancer using transcriptomics, proteomics, phosphosite and metabolomics measurements and contrast estrogen receptor positive and receptor negative patients. DrDimont performs better than drug prediction based on differential protein expression or PageRank when evaluating it on ground truth data from cancer cell lines. We find proteomic and phosphosite layers to carry most information for distinguishing drug response.

AVAILABILITY AND IMPLEMENTATION

DrDimont is available on CRAN: https://cran.r-project.org/package=DrDimont.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MODIG: Integrating Multi-Omics and Multi-Dimensional Gene Network for Cancer Driver Gene Identification based on Graph Attention Network Model

MOTIVATION

Identifying genes that play a causal role in cancer evolution remains one of the biggest challenges in cancer biology. With the accumulation of high-throughput multi-omics data over decades, it becomes a great challenge to effectively integrate these data into the identification of cancer driver genes.

RESULTS

Here, we propose MODIG, a graph attention network (GAT)-based framework to identify cancer driver genes by combining multi-omics pan-cancer data (mutations, copy number variants, gene expression, and methylation levels) with multi-dimensional gene networks. First, we established diverse types of gene relationship maps based on protein-protein interactions (PPI), gene sequence similarity, KEGG pathway co-occurrence, gene co-expression patterns, and Gene Ontology (GO). Then, we constructed a multi-dimensional gene network consisting of approximately 20,000 genes as nodes and five types of gene associations as multiplex edges. We applied a GAT to model within-dimension interactions to generate a gene representation for each dimension based on this graph. Moreover, we introduced a joint learning module to fuse multiple dimension-specific representations to generate general gene representations. Finally, we used the obtained gene representation to perform a semi-supervised driver gene identification task. The experiment results show that MODIG outperforms the baseline models in terms of area under precision-recall curves (AUPR) and area under the receiver operating characteristic curves (AUROC).

AVAILABILITY

The MODIG program is available at https://github.com/zjupgx/modig. The code and data underlying this article are also available on Zenodo, at https://doi.org/10.5281/zenodo.7057241.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Developing whole cell standards for the microbiome field

BACKGROUND

Effective standardisation of the microbiome field is essential to facilitate global translational research and increase the reproducibility of microbiome studies. In this study, we describe the development and validation of a whole cell reference reagent specific to the gut microbiome by the UK National Institute for Biological Standards and Control. We also provide and test a two-step reporting framework to allow microbiome researchers to quickly and accurately validate choices of DNA extraction, sequencing, and bioinformatic pipelines.

RESULTS

Using 20 strains that are commonly found in the gut, we developed a whole cell reference reagent (WC-Gut RR) for the evaluation of the DNA extraction protocols commonly used in microbiome pipelines. DNA was first analysed using the physicochemical measures of yield, integrity, and purity, which demonstrated kits widely differed in the quality of the DNA they produced. Importantly, the combination of the WC-Gut RR and the three physicochemical measures allowed us to differentiate clearly between kit performance. We next assessed the ability of WC-Gut RR to evaluate kit performance in the reconstitution of accurate taxonomic profiles. We applied a four-measure framework consisting of Sensitivity, false-positive relative abundance (FPRA), Diversity, and Similarity as previously described for DNA reagents. Using the WC-Gut RR and these four measures, we could reliably identify the DNA extraction kits' biases when using with both 16S rRNA sequencing and shotgun sequencing. Moreover, when combining this with complementary DNA standards, we could estimate the relative bias contributions of DNA extraction kits vs bioinformatic analysis. Finally, we assessed WC-Gut RR alongside other commercially available reagents. The analysis here clearly demonstrates that reagents of lower complexity, not composed of anaerobic and hard-to-lyse strains from the gut, can artificially inflate the performance of microbiome DNA extraction kits and bioinformatic pipelines.

CONCLUSIONS

We produced a complex whole cell reagent that is specific for the gut microbiome and can be used to evaluate and benchmark DNA extractions in microbiome studies. Used alongside a DNA standard, the NIBSC DNA-Gut-Mix RR helps estimating where biases occur in microbiome pipelines. In the future, we aim to establish minimum thresholds for data quality through an interlaboratory collaborative study. Video Abstract.