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

Exploring the genome of Lactobacillaceae spp. Sy-1 isolated from Heterotrigona itama honey

Background

Honey produced by Heterotrigona itama is highly preferred among consumers due to its high-value as a functional food and beneficial lactic acid bacteria (LAB) reservoir. Fructophilic lactic acid bacteria (FLAB) are a group of LAB with unique growth characteristics and are regarded as promising producers of bioactive compounds. Hence, it is not surprising that LAB, especially FLAB, may be involved with the excellent bioactivity of H. itama honey. With the trending consumer preference for H. itama honey coupled with increasing awareness for healthy food, the genomic background of FLAB isolated from this honey must, therefore, be clearly understood. In this study, one FLAB strain designated as Sy-1 was isolated from freshly collected H. itama honey. Its FLAB behavior and genomic features were investigated to uncover functional genes that could add value to functional food.

Methods

The fructophilic characteristics of strain Sy-1 were determined, and the genome was sequenced using Illumina iSeq100 and Oxford Nanopore. The average nucleotide identity and phylogenetic analyses based on 16S rRNA, 92 core genes, and whole-genome sequence were performed to unravel the phylogenetic position of strain Sy-1. NCBI Prokaryotic Genome Annotation Pipeline annotated the genome, while the EggNOG-mapper, BLASTKoala, and GHOSTKoala were used to add functional genes and pathways information.

Results

Strain Sy-1 prefers D-fructose over D-glucose and actively metabolizes D-glucose in the presence of electron acceptors. Genomic annotation of strain Sy-1 revealed few genes involved in carbohydrate transport and metabolism, and partial deletion of adhE gene, in line with the characteristic of FLAB. The 16S rRNA gene sequence of strain Sy-1 showed the highest similarity to unknown LAB species isolated from the gut of honeybees. The phylogenetic analyses discovered that strain Sy-1 belonged to the Lactobacillaceae family and formed a separate branch closer to type strain from the genera of Acetilactobacillus and Apilactobacillus. The ANI analysis showed the similarity of the closest relative, Apilactobacillus micheneri Hlig3^T. The assembled genome of Sy-1 contains 3 contigs with 2.03 Mbp and a 41% GC content. A total of 1,785 genes were identified, including 1,685 protein-coding genes, 68 tRNA, and 15 rRNA. Interestingly, strain Sy-1 encoded complete genes for the biosynthesis of folate and riboflavin. High-performance liquid chromatography analysis further confirmed the high production of folic acid (1.346 mg/L) by Sy-1.

Discussion

Based on phylogenetic and biochemical characteristics, strain Sy-1 should be classified as a novel genus in the family of Lactobacillaceae and a new member of FLAB. The genome information coupled with experimental studies supported the ability of strain Sy-1 to produce high folic acid. Our collective findings support the suitable application of FLAB strain Sy-1 in the functional food and pharmaceutical industries.

Integrated In Silico Analyses Identify PUF60 and SF3A3 as New Spliceosome-Related Breast Cancer RNA-Binding Proteins

More women are diagnosed with breast cancer (BC) than any other type of cancer. Although large-scale efforts have completely redefined cancer, a cure remains unattainable. In that respect, new molecular functions of the cell should be investigated, such as post-transcriptional regulation. RNA-binding proteins (RBPs) are emerging as critical post-transcriptional modulators of tumorigenesis, but only a few have clear roles in BC. To recognize new putative breast cancer RNA-binding proteins, we performed integrated in silico analyses of all human RBPs (n = 1392) in three major cancer databases and identified five putative BC RBPs (PUF60, TFRC, KPNB1, NSF, and SF3A3), which showed robust oncogenic features related to their genomic alterations, immunohistochemical changes, high interconnectivity with cancer driver genes (CDGs), and tumor vulnerabilities. Interestingly, some of these RBPs have never been studied in BC, but their oncogenic functions have been described in other cancer types. Subsequent analyses revealed PUF60 and SF3A3 as central elements of a spliceosome-related cluster involving RBPs and CDGs. Further research should focus on the mechanisms by which these proteins could promote breast tumorigenesis, with the potential to reveal new therapeutic pathways along with novel drug-development strategies.

Clinicopathological characteristics, survival outcomes, and genetic alterations of younger patients with gastric cancer: Results from the China National Cancer Center and cBioPortal datasets

Background

The survival outcomes of younger patients with gastric cancer (GC) have remained controversial. This study explores the clinicopathological characteristics, survival outcomes, and genetic alterations of younger and older patients with GC.

Methods

Patients with GC were identified from the China National Cancer Center Gastric Cancer Database (NCCGCDB) during 1998–2018. Survival analysis was conducted using Kaplan–Meier estimates and Cox proportional hazards models. Sequencing datasets were enrolled from The Cancer Genome Atlas (TCGA) and Memorial Sloan–Kettering Cancer Center (MSKCC) databases.

Results

A total of 1146 younger (<40 years of age) and 16,988 older (≥40 years of age) cases were included in the study. Younger patients had more poorly differentiated lesions than older patients (53.7% vs. 33.8%, respectively; p < 0.0001), and were more often pTNM stage IV (19.5% vs. 11.8%, respectively; p < 0.001). The 5‐year overall survival (OS) of patients from the NCCGCDB increased from 1998 to 2018. Younger patients with pTNM stage III had a lower survival rate than older patients (p = 0.014), while no differences by age were observed at other stages. The mutation frequency of the LRP1B, GNAS, APC, and KMT2D genes was higher for older than younger patients (p < 0.05 for all genes). While not significantly different, younger patients from the TCGA and MSKCC databases were more likely to have CDH1, RHOA, and CTNNB1 gene mutations.

Conclusions

A stable proportion and improved survival of younger patients were reported using NCCGCDB data. Younger patients with pTNM stage III had lower rates of survival than older patients. Distinct molecular characteristics were identified in younger GC patients which may partly explain the histopathology and prognosis specific to this subpopulation.

Genome wide study of cysteine rich receptor like proteins in Gossypium sp

Cysteine-rich receptor-like-kinases (CRKs), a transmembrane subfamily of receptor-like kinase, play crucial roles in plant adaptation. As such cotton is the major source of fiber for the textile industry, but environmental stresses are limiting its growth and production. Here, we have performed a deep computational analysis of CRKs in five Gossypium species, including G. arboreum (60 genes), G. raimondii (74 genes), G. herbaceum (65 genes), G. hirsutum (118 genes), and G. barbadense (120 genes). All identified CRKs were classified into 11 major classes and 43 subclasses with the finding of several novel CRK-associated domains including ALMT, FUSC_2, Cript, FYVE, and Pkinase. Of these, DUF26_DUF26_Pkinase_Tyr was common and had elevated expression under different biotic and abiotic stresses. Moreover, the 35 land plants comparison identified several new CRKs domain-architectures. Likewise, several SNPs and InDels were observed in CLCuD resistant G. hirsutum. The miRNA target side prediction and their expression profiling in different tissues predicted miR172 as a major CRK regulating miR. The expression profiling of CRKs identified multiple clusters with co-expression under certain stress conditions. The expression analysis under CLCuD highlighted the role of GhCRK057, GhCRK059, GhCRK058, and GhCRK081 in resistant accession. Overall, these results provided primary data for future potential functional analysis as well as a reference study for other agronomically important crops.

Machine learning methods for prediction of cancer driver genes: a survey paper

Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.

A near-chromosome level genome assembly of the European hoverfly, Sphaerophoria rueppellii (Diptera: Syrphidae), provides comparative insights into insecticide resistance-related gene family evolution

BACKGROUND

Sphaerophoria rueppellii, a European species of hoverfly, is a highly effective beneficial predator of hemipteran crop pests including aphids, thrips and coleopteran/lepidopteran larvae in integrated pest management (IPM) programmes. It is also a key pollinator of a wide variety of important agricultural crops. No genomic information is currently available for S. rueppellii. Without genomic information for such beneficial predator species, we are unable to perform comparative analyses of insecticide target-sites and genes encoding metabolic enzymes potentially responsible for insecticide resistance, between crop pests and their predators. These metabolic mechanisms include several gene families - cytochrome P450 monooxygenases (P450s), ATP binding cassette transporters (ABCs), glutathione-S-transferases (GSTs), UDP-glycosyltransferases (UGTs) and carboxyl/choline esterases (CCEs).

METHODS AND FINDINGS

In this study, a high-quality near-chromosome level de novo genome assembly (as well as a mitochondrial genome assembly) for S. rueppellii has been generated using a hybrid approach with PacBio long-read and Illumina short-read data, followed by super scaffolding using Hi-C data. The final assembly achieved a scaffold N50 of 87Mb, a total genome size of 537.6Mb and a level of completeness of 96% using a set of 1,658 core insect genes present as full-length genes. The assembly was annotated with 14,249 protein-coding genes. Comparative analysis revealed gene expansions of CYP6Zx P450s, epsilon-class GSTs, dietary CCEs and multiple UGT families (UGT37/302/308/430/431). Conversely, ABCs, delta-class GSTs and non-CYP6Zx P450s showed limited expansion. Differences were seen in the distributions of resistance-associated gene families across subfamilies between S. rueppellii and some hemipteran crop pests. Additionally, S. rueppellii had larger numbers of detoxification genes than other pollinator species.

CONCLUSION AND SIGNIFICANCE

This assembly is the first published genome for a predatory member of the Syrphidae family and will serve as a useful resource for further research into selectivity and potential tolerance of insecticides by beneficial predators. Furthermore, the expansion of some gene families often linked to insecticide resistance and selectivity may be an indicator of the capacity of this predator to detoxify IPM selective insecticides. These findings could be exploited by targeted insecticide screens and functional studies to increase effectiveness of IPM strategies, which aim to increase crop yields by sustainably and effectively controlling pests without impacting beneficial predator populations.

Comparative analysis of the daily brain transcriptomes of Asian particolored bat

Daily rhythms are found in almost all organisms, and they comprise one of the most basic characteristics of living things. Daily rhythms are generated and mainly regulated by circadian clock. Bats have attracted interest from researchers because of their unique biological characteristics. However, little is known about the molecular underpinnings of daily rhythms in bats. In this study, we used RNA-Seq to uncover the daily rhythms of gene expression in the brains of Asian particolored bats over the 24-h day. Accordingly, four collected time points corresponding to four biological states, rest, sleep, wakefulness, and active, were selected. Several groups of genes with different expression levels in these four states were obtained suggested that different physiological processes were active at various biological states, including drug metabolism, signaling pathways, and the circadian rhythm. Furthermore, downstream analysis of all differentially expressed genes in these four states suggested that groups of genes showed daily rhythms in the bat brain. Especially for Per1, an important circadian clock gene was identified with rhythmic expression in the brain of Asian particolored bat. In summary, our study provides an overview of the brain transcriptomic differences in different physiological states over a 24-h cycle.

Clinical utility of PDX cohorts to reveal biomarkers of intrinsic resistance and clonal architecture changes underlying acquired resistance to cetuximab in HNSCC

Cetuximab is a widely used drug for treating head and neck squamous cell carcinomas (HNSCCs); however, it provides restricted clinical benefits, and its response duration is limited by drug resistance. Here, we conducted randomized “Phase II-like clinical trials” of 49 HNSCC PDX models and reveal multiple informative biomarkers for intrinsic resistance to cetuximab (e.g., amplification of ANKH, up-regulation of PARP3). After validating these intrinsic resistance biomarkers in another HNSCC PDX cohort (61 PDX models), we generated acquired cetuximab resistance PDX models and analyzed them to uncover resistance mechanisms. Whole exome sequencing and transcriptome sequencing revealed diverse patterns of clonal selection in acquired resistant PDXs, including the emergence of subclones with strongly activated RAS/MAPK. Extending these insights, we show that a combination of a RAC1/RAC3 dual-target inhibitor and cetuximab could overcome acquired cetuximab resistance in vitro and in vivo. Beyond revealing intrinsic resistance biomarkers, our PDX-based study shows how clonal architecture changes underlying acquired resistance can be targeted to expand the therapeutic utility of this important drug to more HNSCC patients.

Comparative analysis of the Mercenaria mercenaria genome provides insights into the diversity of transposable elements and immune molecules in bivalve mollusks

BACKGROUND

The hard clam Mercenaria mercenaria is a major marine resource along the Atlantic coasts of North America and has been introduced to other continents for resource restoration or aquaculture activities. Significant mortality events have been reported in the species throughout its native range as a result of diseases (microbial infections, leukemia) and acute environmental stress. In this context, the characterization of the hard clam genome can provide highly needed resources to enable basic (e.g., oncogenesis and cancer transmission, adaptation biology) and applied (clam stock enhancement, genomic selection) sciences.

RESULTS

Using a combination of long and short-read sequencing technologies, a 1.86 Gb chromosome-level assembly of the clam genome was generated. The assembly was scaffolded into 19 chromosomes, with an N50 of 83 Mb. Genome annotation yielded 34,728 predicted protein-coding genes, markedly more than the few other members of the Venerida sequenced so far, with coding regions representing only 2% of the assembly. Indeed, more than half of the genome is composed of repeated elements, including transposable elements. Major chromosome rearrangements were detected between this assembly and another recent assembly derived from a genetically segregated clam stock. Comparative analysis of the clam genome allowed the identification of a marked diversification in immune-related proteins, particularly extensive tandem duplications and expansions in tumor necrosis factors (TNFs) and C1q domain-containing proteins, some of which were previously shown to play a role in clam interactions with infectious microbes. The study also generated a comparative repertoire highlighting the diversity and, in some instances, the specificity of LTR-retrotransposons elements, particularly Steamer elements in bivalves.

CONCLUSIONS

The diversity of immune molecules in M. mercenaria may allow this species to cope with varying and complex microbial and environmental landscapes. The repertoire of transposable elements identified in this study, particularly Steamer elements, should be a prime target for the investigation of cancer cell development and transmission among bivalve mollusks.

Deciphering the link between Diabetes mellitus and SARS-CoV-2 infection through differential targeting of microRNAs in the human pancreas

PURPOSE

Coronavirus Disease 2019 (COVID-19) severity and Diabetes mellitus affect each other bidirectionally. However, the cause of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection on the incidence of diabetes is unclear. In the SARS-CoV-2-infected cells, host microRNAs (miRNAs) may target the native gene transcripts as well as the viral genomic and subgenomic RNAs. Here, we investigated the role of miRNAs in linking Diabetes to SARS-CoV-2 infection in the human pancreas.

METHODS

Differential gene expression and disease enrichment analyses were performed on an RNA-Seq dataset of human embryonic stem cell-derived (hESC) mock-infected and SARS-CoV-2-infected pancreatic organoids to obtain the dysregulated Diabetes-associated genes. The miRNA target prediction for the Diabetes-associated gene transcripts and the SARS-CoV-2 RNAs has been made to determine the common miRNAs targeting them. Minimum Free Energy (MFE) analysis was done to identify the miRNAs, preferably targeting SARS-CoV-2 RNAs over the Diabetes-associated gene transcripts.

RESULTS

The gene expression and disease enrichment analyses of the RNA-Seq data have revealed five biomarker genes, i.e., CP, SOCS3, AGT, PSMB8 and CFB that are associated with Diabetes and get significantly upregulated in the pancreas following SARS-CoV-2-infection. Four miRNAs, i.e., hsa-miR-298, hsa-miR-3925-5p, hsa-miR-4691-3p and hsa-miR-5196-5p, showed preferential targeting of the SARS-CoV-2 genome over the cell's Diabetes-associated messenger RNAs (mRNAs) in the human pancreas.

CONCLUSION

Our study proposes that the differential targeting of the Diabetes-associated host genes by the miRNAs may lead to diabetic complications or new-onset Diabetes that can worsen the condition of COVID-19 patients.

Decoding the sorghum methylome: understanding epigenetic contributions to agronomic traits

DNA methylation is a chromatin modification that plays an essential role in regulating gene expression and genome stability and it is typically associated with gene silencing and heterochromatin. Owing to its heritability, alterations in the patterns of DNA methylation have the potential to provide for epigenetic inheritance of traits. Contemporary epigenomic technologies provide information beyond sequence variation and could supply alternative sources of trait variation for improvement in crops such as sorghum. Yet, compared with other species such as maize and rice, the sorghum DNA methylome is far less well understood. The distribution of CG, CHG, and CHH methylation in the genome is different compared with other species. CG and CHG methylation levels peak around centromeric segments in the sorghum genome and are far more depleted in the gene dense chromosome arms. The genes regulating DNA methylation in sorghum are also yet to be functionally characterised; better understanding of their identity and functional analysis of DNA methylation machinery mutants in diverse genotypes will be important to better characterise the sorghum methylome. Here, we catalogue homologous genes encoding methylation regulatory enzymes in sorghum based on genes in Arabidopsis, maize, and rice. Discovering variation in the methylome may uncover epialleles that provide extra information to explain trait variation and has the potential to be applied in epigenome-wide association studies or genomic prediction. DNA methylation can also improve genome annotations and discover regulatory elements underlying traits. Thus, improving our knowledge of the sorghum methylome can enhance our understanding of the molecular basis of traits and may be useful to improve sorghum performance.

Analysis of Novel Variants Associated with Three Human Ovarian Cancer Cell Lines

Background:

Identification of mutations is of great significance in cancer research, as it can contribute to development of therapeutic strategies and prevention of cancer formation. Ovarian cancer is one of the leading cancer-related causes of death in Taiwan. Accumulation of genetic mutations can lead to cancer.

Objective:

We utilized whole-exome sequencing to explore cancer-associated missense variants in three human ovarian cancer cell lines derived from Taiwanese patients.

Methods:

We use (i) cell line whole-exome sequencing data, (ii) 188 patients’ whole-exome sequencing data, and (iii) use of in vitro experiments to verify predicted variant results. We establish an effective analysis workflow for discovery of novel ovarian cancer variants, comprising three steps: (i) use of public databases and in-house hospital data to select novel variants (ii) investigation of protein structural stability caused by genetic mutations, and (iii) use of in vitro experiments to verify predictions.

Results:

Our study enumerated 296 novel variants by imposing specific criteria and using sophisticated bioinformatics tools for further analysis. Eleven and 54 missense novel variants associated with cancerous and non-cancerous genes, respectively, were identified. We show that 13 missense mutations affect the stability of protein 3D structure, while 11 disease-causing novel variants were confirmed by PCR sequencing. Among these, ten variants were predicted to be pathogenic, while the pathogenicity of one was uncertain.

Conclusion:

We confirm that novel variant genes play a crucial role in ovarian cancer patients, with 11 novel variants that may promote progression and development of ovarian cancer.

Thermophiles and carbohydrate-active enzymes (CAZymes) in biofilm microbial consortia that decompose lignocellulosic plant litters at high temperatures

The SKY hot spring is a unique site filled with a thick layer of plant litter. With the advancement of next-generation sequencing, it is now possible to mine many new biocatalyst sequences. In this study, we aimed to (i) identify the metataxonomic of prokaryotes and eukaryotes in microbial mats using 16S and 18S rRNA markers, (ii) and explore carbohydrate degrading enzymes (CAZymes) that have a high potential for future applications. Green microbial mat, predominantly photosynthetic bacteria, was attached to submerged or floating leaves litter. At the spring head, the sediment mixture consisted of plant debris, predominantly brownish-reddish gelatinous microbial mat, pale tan biofilm, and grey-white filament biofilm. The population in the spring head had a higher percentage of archaea and hyperthermophiles than the green mat. Concurrently, we cataloged nearly 10,000 sequences of CAZymes in both green and brown biofilms using the shotgun metagenomic sequencing approach. These sequences include β-glucosidase, cellulase, xylanase, α-N-arabinofuranosidase, α-l-arabinofuranosidase, and other CAZymes. In conclusion, this work elucidated that SKY is a unique hot spring due to its rich lignocellulosic material, often absent in other hot springs. The data collected from this study serves as a repository of new thermostable macromolecules, in particular families of glycoside hydrolases.

The era of Cas12 and Cas13 CRISPR-based disease diagnosis

Clustered regularly interspaced short palindromic repeats (CRISPR) and associated protein (Cas) systems, since their discovery, have found growing applications in cell imaging, transcription modulation, therapeutics and diagnostics. Discovery of Cas12 and Cas13 have brought a new dimension to the field of disease diagnosis. These endonucleases have been extensively used for diagnosis of viral diseases in humans and animals and to a lesser extent in plants. The exigency of SARS-CoV-2 pandemic has highlighted the potential of CRISPR-Cas systems and sparked the development of innovative point-of-care diagnostic technologies. Rapid adaptation of CRISPR-chemistry combined with sensitive read-outs for emerging pathogens make them ideal candidates for detection and management of diseases in future. CRISPR-based approaches have been recruited for the challenging task of cancer detection and prognosis. It stands to reason that the field of CRISPR-Cas-based diagnosis is likely to expand with Cas12 and Cas13 playing a pivotal role. Here we focus exclusively on Cas12- and Cas13-based molecular diagnosis in humans, animals and plants including the detection of SARS-coronavirus. The CRISPR-based diagnosis of plant and animal diseases have not found adequate mention in previous reviews. We discuss various advancements, the potential shortfalls and challenges in the widespread adaptation of this technology for disease diagnosis.

Single-cell RNA sequencing reveals induction of distinct trained immunity programs in human monocytes

Trained immunity refers to the long-lasting memory traits of innate immunity. Recent studies have shown that trained immunity is orchestrated by sustained changes in epigenetic marks and metabolic pathways, leading to an altered transcriptional response towards a second challenge. However, the potential heterogeneity of trained immunity induction in innate immune cells has not been explored. In this study, we demonstrate cellular transcriptional programs to four different inducers of trained immunity in monocyte populations at single-cell resolution. Specifically, we identified three monocyte subpopulations upon the induction of trained immunity, and replicated these findings in an in vivo study. In addition, we found gene signatures consistent with these functional programs in ulcerative colitis, sepsis and COVID-19 patients, suggesting the impact of trained immunity programs in immune-mediated diseases.

Distribution of soil viruses across China and their potential role in phosphorous metabolism

BACKGROUND

Viruses are the most abundant biological entities on the planet and drive biogeochemical cycling on a global scale. Our understanding of biogeography of soil viruses and their ecological functions lags significantly behind that of Bacteria and Fungi. Here, a viromic approach was used to investigate the distribution and ecological functions of viruses from 19 soils across China.

RESULTS

Soil viral community were clustered more significantly by geographical location than type of soil (agricultural and natural). Three clusters of viral communities were identified from North, Southeast and Southwest regions; these clusters differentiated using taxonomic composition and were mainly driven by geographic location and climate factors. A total of 972 viral populations (vOTUs) were detected spanning 23 viral families from the 19 viromes. Phylogenetic analyses of the phoH gene showed a remarkable diversity and the distribution of viral phoH genes was more dependent on the environment. Notably, five proteins involved in phosphorus (P) metabolism-related nucleotide synthesis functions, including dUTPase, MazG, PhoH, Thymidylate synthase complementing protein (Thy1), and Ribonucleoside reductase (RNR), were mainly identified in agricultural soils.

CONCLUSIONS

The present work revealed that soil viral communities were distributed across China according to geographical location and climate factors. In addition, P metabolism genes encoded by these viruses probably drive the synthesis of nucleotides for their own genomes inside bacterial hosts, thereby affecting P cycling in the soil ecosystems.

CITEMOXMBD: A flexible single-cell multimodal omics analysis framework to reveal the heterogeneity of immune cells

Simultaneous measurement of multiple modalities in single-cell analysis, represented by CITE-seq, is a promising approach to link transcriptional changes to cellular phenotype and function, requiring new computational methods to define cellular subtypes and states based on multiple data types. Here, we design a flexible single-cell multimodal analysis framework, called CITEMO, to integrate the transcriptome and antibody-derived tags (ADT) data to capture cell heterogeneity from the multi omics perspective. CITEMO uses Principal Component Analysis (PCA) to obtain a low-dimensional representation of the transcriptome and ADT, respectively, and then employs PCA again to integrate these low-dimensional multimodal data for downstream analysis. To investigate the effectiveness of the CITEMO framework, we apply CITEMO to analyse the cell subtypes of Cord Blood Mononuclear Cells (CBMC) samples. Results show that the CITEMO framework can comprehensively analyse single-cell multimodal samples and accurately identify cell subtypes. Besides, we find some specific immune cells that co-express multiple ADT markers. To better describe the co-expression phenomenon, we introduce the co-expression entropy to measure the heterogeneous distribution of the ADT combinations. To further validate the robustness of the CITEMO framework, we analyse Human Bone Marrow Cell (HBMC) samples and identify different states of the same cell type. CITEMO has an excellent performance in identifying cell subtypes and states for multimodal omics data. We suggest that the flexible design idea of CITEMO can be an inspiration for other single-cell multimodal tasks. The complete source code and dataset of the CITEMO framework can be obtained from https://github.com/studentiz/CITEMO.

Comprehensive metabolomics expands precision medicine for triple-negative breast cancer

Metabolic reprogramming is a hallmark of cancer. However, systematic characterizations of metabolites in triple-negative breast cancer (TNBC) are still lacking. Our study profiled the polar metabolome and lipidome in 330 TNBC samples and 149 paired normal breast tissues to construct a large metabolomic atlas of TNBC. Combining with previously established transcriptomic and genomic data of the same cohort, we conducted a comprehensive analysis linking TNBC metabolome to genomics. Our study classified TNBCs into three distinct metabolomic subgroups: C1, characterized by the enrichment of ceramides and fatty acids; C2, featured with the upregulation of metabolites related to oxidation reaction and glycosyl transfer; and C3, having the lowest level of metabolic dysregulation. Based on this newly developed metabolomic dataset, we refined previous TNBC transcriptomic subtypes and identified some crucial subtype-specific metabolites as potential therapeutic targets. The transcriptomic luminal androgen receptor (LAR) subtype overlapped with metabolomic C1 subtype. Experiments on patient-derived organoid and xenograft models indicate that targeting sphingosine-1-phosphate (S1P), an intermediate of the ceramide pathway, is a promising therapy for LAR tumors. Moreover, the transcriptomic basal-like immune-suppressed (BLIS) subtype contained two prognostic metabolomic subgroups (C2 and C3), which could be distinguished through machine-learning methods. We show that N-acetyl-aspartyl-glutamate is a crucial tumor-promoting metabolite and potential therapeutic target for high-risk BLIS tumors. Together, our study reveals the clinical significance of TNBC metabolomics, which can not only optimize the transcriptomic subtyping system, but also suggest novel therapeutic targets. This metabolomic dataset can serve as a useful public resource to promote precision treatment of TNBC.

Rare transmission of commensal and pathogenic bacteria in the gut microbiome of hospitalized adults

Bacterial bloodstream infections are a major cause of morbidity and mortality among patients undergoing hematopoietic cell transplantation (HCT). Although previous research has demonstrated that pathogens may translocate from the gut microbiome into the bloodstream to cause infections, the mechanisms by which HCT patients acquire pathogens in their microbiome have not yet been described. Here, we use linked-read and short-read metagenomic sequencing to analyze 401 stool samples collected from 149 adults undergoing HCT and hospitalized in the same unit over three years, many of whom were roommates. We use metagenomic assembly and strain-specific comparison methods to search for high-identity bacterial strains, which may indicate transmission between the gut microbiomes of patients. Overall, the microbiomes of patients who share time and space in the hospital do not converge in taxonomic composition. However, we do observe six pairs of patients who harbor identical or nearly identical strains of the pathogen Enterococcus faecium, or the gut commensals Akkermansia muciniphila and Hungatella hathewayi. These shared strains may result from direct transmission between patients who shared a room and bathroom, acquisition from a common hospital source, or transmission from an unsampled intermediate. We also identify multiple patients with identical strains of species commonly found in commercial probiotics, including Lactobacillus rhamnosus and Streptococcus thermophilus. In summary, our findings indicate that sharing of identical pathogens between the gut microbiomes of multiple patients is a rare phenomenon. Furthermore, the observed potential transmission of commensal, immunomodulatory microbes suggests that exposure to other humans may contribute to microbiome reassembly post-HCT.

Here, Siranosian et al. provide evidence for rare transmission of commensal and pathogenic bacteria between the microbiomes of hospitalized adults, with important factors being roommate overlap and exposure to broad-spectrum antibiotics.

Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the Network of Cancer Genes (NCG) resource

Background

Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development.

Results

Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation.

Conclusions

Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02607-z.

SARS-CoV-2 variant detection at a university dormitory using wastewater genomic tools

In the Fall of 2020, university campuses in the United States resumed on-campus instruction and implemented wastewater monitoring for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While quantitative polymerase chain reaction (qPCR) tests were deployed successfully to detect viral RNA in wastewater across campuses, the feasibility of detecting viral variants from a residential building like a dormitory was unclear. Here, we demonstrate that wastewater surveillance from a dormitory with at least three infected students could lead to the identification of viral genomes with more than 95% coverage. Our results indicate that viral variant detection from wastewater is achievable at a dormitory and that coronavirus disease 2019 (COVID-19) wastewater surveillance programs will benefit from the implementation of viral whole genome sequencing at universities.

Proximity ligation strategy for the genomic reconstruction of microbial communities associated with the ectoparasite Caligus rogercresseyi

The sea louse Caligus rogercresseyi has become one of the main constraints for the sustainable development of salmon aquaculture in Chile. Although this parasite's negative impacts are well recognized by the industry, some novel potential threats remain unnoticed. The recent sequencing of the C. rogercresseyi genome revealed a large bacterial community associated with the sea louse, however, it is unknown if these microorganisms should become a new focus of sanitary concern. Herein, chromosome proximity ligation (Hi-C) coupled with long-read sequencing were used for the genomic reconstruction of the C. rogercresseyi microbiota. Through deconvolution analysis, we were able to assemble and characterize 413 bacterial genome clusters, including six bacterial genomes with more than 80% of completeness. The most represented bacterial genome belonged to the fish pathogen Tenacibacullum ovolyticum (97.87% completeness), followed by Dokdonia sp. (96.71% completeness). This completeness allowed identifying 21 virulence factors (VF) within the T. ovolyticum genome and four antibiotic resistance genes (ARG). Notably, genomic pathway reconstruction analysis suggests putative metabolic complementation mechanisms between C. rogercresseyi and its associated microbiota. Taken together, our data highlight the relevance of Hi-C techniques to discover pathogenic bacteria, VF, and ARGs and also suggest novel host-microbiota mutualism in sea lice biology.

Screening and Comprehensive Analysis of Cancer-Associated tRNA-Derived Fragments

tRNA-derived fragments (tRFs) constitute a novel class of small non-coding RNA cleaved from tRNAs. In recent years, researches have shown the regulatory roles of a few tRFs in cancers, illuminating a new direction for tRF-centric cancer researches. Nonetheless, more specific screening of tRFs related to oncogenesis pathways, cancer progression stages and cancer prognosis is continuously demanded to reveal the landscape of the cancer-associated tRFs. In this work, by combining the clinical information recorded in The Cancer Genome Atlas (TCGA) and the tRF expression profiles curated by MINTbase v2.0, we systematically screened 1,516 cancer-associated tRFs (ca-tRFs) across seven cancer types. The ca-tRF set collectively combined the differentially expressed tRFs between cancer samples and control samples, the tRFs significantly correlated with tumor stage and the tRFs significantly correlated with patient survival. By incorporating our previous tRF-target dataset, we found the ca-tRFs tend to target cancer-associated genes and onco-pathways like ATF6-mediated unfolded protein response, angiogenesis, cell cycle process regulation, focal adhesion, PI3K-Akt signaling pathway, cellular senescence and FoxO signaling pathway across multiple cancer types. And cell composition analysis implies that the expressions of ca-tRFs are more likely to be correlated with T-cell infiltration. We also found the ca-tRF expression pattern is informative to prognosis, suggesting plausible tRF-based cancer subtypes. Together, our systematic analysis demonstrates the potentially extensive involvements of tRFs in cancers, and provides a reasonable list of cancer-associated tRFs for further investigations.

A scaffold-level genome assembly of a minute pirate bug, Orius laevigatus (Hemiptera: Anthocoridae), and a comparative analysis of insecticide resistance-related gene families with hemipteran crop pests

BACKGROUND

Orius laevigatus, a minute pirate bug, is a highly effective beneficial predator of crop pests including aphids, spider mites and thrips in integrated pest management (IPM) programmes. No genomic information is currently available for O. laevigatus, as is the case for the majority of beneficial predators which feed on crop pests. In contrast, genomic information for crop pests is far more readily available. The lack of publicly available genomes for beneficial predators to date has limited our ability to perform comparative analyses of genes encoding potential insecticide resistance mechanisms between crop pests and their predators. These mechanisms include several gene/protein families including cytochrome P450s (P450s), ATP binding cassette transporters (ABCs), glutathione S-transferases (GSTs), UDP-glucosyltransferases (UGTs) and carboxyl/cholinesterases (CCEs).

METHODS AND FINDINGS

In this study, a high-quality scaffold level de novo genome assembly for O. laevigatus has been generated using a hybrid approach with PacBio long-read and Illumina short-read data. The final assembly achieved a scaffold N50 of 125,649 bp and a total genome size of 150.98 Mb. The genome assembly achieved a level of completeness of 93.6% using a set of 1658 core insect genes present as full-length genes. Genome annotation identified 15,102 protein-coding genes - 87% of which were assigned a putative function. Comparative analyses revealed gene expansions of sigma class GSTs and CYP3 P450s. Conversely the UGT gene family showed limited expansion. Differences were seen in the distributions of resistance-associated gene families at the subfamily level between O. laevigatus and some of its targeted crop pests. A target site mutation in ryanodine receptors (I4790M, PxRyR) which has strong links to diamide resistance in crop pests and had previously only been identified in lepidopteran species was found to also be present in hemipteran species, including O. laevigatus.

CONCLUSION AND SIGNIFICANCE

This assembly is the first published genome for the Anthocoridae family and will serve as a useful resource for further research into target-site selectivity issues and potential resistance mechanisms in beneficial predators. Furthermore, the expansion of gene families often linked to insecticide resistance may be an indicator of the capacity of this predator to detoxify selective insecticides. These findings could be exploited by targeted pesticide screens and functional studies to increase effectiveness of IPM strategies, which aim to increase crop yields by sustainably, environmentally-friendly and effectively control pests without impacting beneficial predator populations.

Genome-wide identification of the Liriodendron chinense WRKY gene family and its diverse roles in response to multiple abiotic stress

BACKGROUND

Liriodendron chinense (Lchi) is a tree species within the Magnoliaceae family and is considered a basal angiosperm. The too low or high temperature or soil drought will restrict its growth as the adverse environmental conditions, thus improving L. chinense abiotic tolerance was the key issues to study. WRKYs are a major family of plant transcription factors known to often be involved in biotic and abiotic stress responses. So far, it is still largely unknown if and how the LchiWRKY gene family is tied to regulating L. chinense stress responses. Therefore, studying the involvement of the WRKY gene family in abiotic stress regulation in L. chinense could be very informative in showing how this tree deals with such stressful conditions.

RESULTS

In this research, we performed a genome-wide analysis of the Liriodendron chinense (Lchi) WRKY gene family, studying their classification relationships, gene structure, chromosomal locations, gene duplication, cis-element, and response to abiotic stress. The 44 members of the LchiWRKY gene family contain a significant amount of sequence diversity, with their lengths ranging from 525 bp to 40,981 bp. Using classification analysis, we divided the 44 LchiWRKY genes into three phylogenetic groups (I, II, II), with group II then being further divided into five subgroups (IIa, IIb, IIc, IId, IIe). Comparative phylogenetic analysis including the WRKY families from 17 plant species suggested that LchiWRKYs are closely related to the Magnolia Cinnamomum kanehirae WRKY family, and has fewer family members than higher plants. We found the LchiWRKYs to be evenly distributed across 15 chromosomes, with their duplication events suggesting that tandem duplication may have played a major role in LchiWRKY gene expansion model. A Ka/Ks analysis indicated that they mainly underwent purifying selection and distributed in the group IId. Motif analysis showed that LchiWRKYs contained 20 motifs, and different phylogenetic groups contained conserved motif. Gene ontology (GO) analysis showed that LchiWRKYs were mainly enriched in two categories, i.e., biological process and molecular function. Two group IIc members (LchiWRKY10 and LchiWRKY37) contain unique WRKY element sequence variants (WRKYGKK and WRKYGKS). Gene structure analysis showed that most LchiWRKYs possess 3 exons and two different types of introns: the R- and V-type which are both contained within the WRKY domain (WD). Additional promoter cis-element analysis indicated that 12 cis-elements that play different functions in environmental adaptability occur across all LchiWRKY groups. Heat, cold, and drought stress mainly induced the expression of group II and I LchiWRKYs, some of which had undergone gene duplication during evolution, and more than half of which had three exons. LchiWRKY33 mainly responded to cold stress and LchiWRKY25 mainly responded to heat stress, and LchiWRKY18 mainly responded to drought stress, which was almost 4-fold highly expressed, while 5 LchiWRKYs (LchiWRKY5, LchiWRKY23, LchiWRKY14, LchiWRKY27, and LchiWRKY36) responded equally three stresses with more than 6-fold expression. Subcellular localization analysis showed that all LchiWRKYs were localized in the nucleus, and subcellular localization experiments of LchiWRKY18 and 36 also showed that these two transcription factors were expressed in the nucleus.

CONCLUSIONS

This study shows that in Liriodendron chinense, several WRKY genes like LchiWRKY33, LchiWRKY25, and LchiWRKY18, respond to cold or heat or drought stress, suggesting that they may indeed play a role in regulating the tree's response to such conditions. This information will prove a pivotal role in directing further studies on the function of the LchiWRKY gene family in abiotic stress response and provides a theoretical basis for popularizing afforestation in different regions of China.

Prediction of biomarkers and therapeutic combinations for anti-PD-1 immunotherapy using the global gene network association

Owing to a lack of response to the anti-PD1 therapy for most cancer patients, we develop a network approach to infer genes, pathways, and potential therapeutic combinations that are associated with tumor response to anti-PD1. Here, our prediction identifies genes and pathways known to be associated with anti-PD1, and is further validated by 6 CRISPR gene sets associated with tumor resistance to cytotoxic T cells and targets of the 36 compounds that have been tested in clinical trials for combination treatments with anti-PD1. Integration of our top prediction and TCGA data identifies hundreds of genes whose expression and genetic alterations that could affect response to anti-PD1 in each TCGA cancer type, and the comparison of these genes across cancer types reveals that the tumor immunoregulation associated with response to anti-PD1 would be tissue-specific. In addition, the integration identifies the gene signature to calculate the MHC I association immunoscore (MIAS) that shows a good correlation with patient response to anti-PD1 for 411 melanoma samples complied from 6 cohorts. Furthermore, mapping drug target data to the top genes in our association prediction identifies inhibitors that could potentially enhance tumor response to anti-PD1, such as inhibitors of the encoded proteins of CDK4, GSK3B, and PTK2.

Pan-Cancer DNA Methylation Analysis and Tumor Origin Identification of Carcinoma of Unknown Primary Site Based on Multi-Omics

The metastatic cancer of unknown primary (CUP) sites remains a leading cause of cancer death with few therapeutic options. The aberrant DNA methylation (DNAm) is the most important risk factor for cancer, which has certain tissue specificity. However, how DNAm alterations in tumors differ among the regulatory network of multi-omics remains largely unexplored. Therefore, there is room for improvement in our accuracy in the prediction of tumor origin sites and a need for better understanding of the underlying mechanisms. In our study, an integrative analysis based on multi-omics data and molecular regulatory network uncovered genome-wide methylation mechanism and identified 23 epi-driver genes. Apart from the promoter region, we also found that the aberrant methylation within the gene body or intergenic region was significantly associated with gene expression. Significant enrichment analysis of the epi-driver genes indicated that these genes were highly related to cellular mechanisms of tumorigenesis, including T-cell differentiation, cell proliferation, and signal transduction. Based on the ensemble algorithm, six CpG sites located in five epi-driver genes were selected to construct a tissue-specific classifier with a better accuracy (>95%) using TCGA datasets. In the independent datasets and the metastatic cancer datasets from GEO, the accuracy of distinguishing tumor subtypes or original sites was more than 90%, showing better robustness and stability. In summary, the integration analysis of large-scale omics data revealed complex regulation of DNAm across various cancer types and identified the epi-driver genes participating in tumorigenesis. Based on the aberrant methylation status located in epi-driver genes, a classifier that provided the highest accuracy in tracing back to the primary sites of metastatic cancer was established. Our study provides a comprehensive and multi-omics view of DNAm-associated changes across cancer types and has potential for clinical application.

MYB transcription factor family in sweet cherry (Prunus avium L.): genome-wide investigation, evolution, structure, characterization and expression patterns

BACK GROUND

MYB Transcription factors (TFs) are most imperative and largest gene family in plants, which participate in development, metabolism, defense, differentiation and stress response. The MYB TFs has been studied in various plant species. However, comprehensive studies of MYB gene family in the sweet cherry (Prunus avium L.) are still unknown.

RESULTS

In the current study, a total of 69 MYB genes were investigated from sweet cherry genome and classified into 28 subfamilies (C1-C28 based on phylogenetic and structural analysis). Microcollinearity analysis revealed that dispersed duplication (DSD) events might play an important role in the MYB genes family expansion. Chromosomal localization, the synonymous (Ks) and nonsynonymous (Ka) analysis, molecular characteristics (pI, weight and length of amino acids) and subcellular localization were accomplished using several bioinformatics tools. Furthermore, the members of distinct subfamilies have diverse cis-acting regions, conserved motifs, and intron-exon architectures, indicating functional heterogeneity in the MYB family. Moreover, the transcriptomic data exposed that MYB genes might play vital role in bud dormancy. The quantitative real-time qRT-PCR was carried out and the expression pattern indicated that MYB genes significantly expressed in floral bud as compared to flower and fruit.

CONCLUSION

Our comprehensive findings provide supportive insights into the evolutions, expansion complexity and functionality of PavMYB genes. These PavMYB genes should be further investigated as they seem to be brilliant candidates for dormancy manipulation in sweet cherry.

Key transcription factors mediating cocaine-induced plasticity in the nucleus accumbens

Repeated cocaine use induces coordinated changes in gene expression that drive plasticity in the nucleus accumbens (NAc), an important component of the brain's reward circuitry, and promote the development of maladaptive, addiction-like behaviors. Studies on the molecular basis of cocaine action identify transcription factors, a class of proteins that bind to specific DNA sequences and regulate transcription, as critical mediators of this cocaine-induced plasticity. Early methods to identify and study transcription factors involved in addiction pathophysiology primarily relied on quantifying the expression of candidate genes in bulk brain tissue after chronic cocaine treatment, as well as conventional overexpression and knockdown techniques. More recently, advances in next generation sequencing, bioinformatics, cell-type-specific targeting, and locus-specific neuroepigenomic editing offer a more powerful, unbiased toolbox to identify the most important transcription factors that drive drug-induced plasticity and to causally define their downstream molecular mechanisms. Here, we synthesize the literature on transcription factors mediating cocaine action in the NAc, discuss the advancements and remaining limitations of current experimental approaches, and emphasize recent work leveraging bioinformatic tools and neuroepigenomic editing to study transcription factors involved in cocaine addiction.

A gradient tree boosting and network propagation derived pan-cancer survival network of the tumor microenvironment

Predicting cancer survival from molecular data is an important aspect of biomedical research because it allows quantifying patient risks and thus individualizing therapy. We introduce XGBoost tree ensemble learning to predict survival from transcriptome data of 8,024 patients from 25 different cancer types and show highly competitive performance with state-of-the-art methods. To further improve plausibility of the machine learning approach we conducted two additional steps. In the first step, we applied pan-cancer training and showed that it substantially improves prognosis compared with cancer subtype-specific training. In the second step, we applied network propagation and inferred a pan-cancer survival network consisting of 103 genes. This network highlights cross-cohort features and is predictive for the tumor microenvironment and immune status of the patients. Our work demonstrates that pan-cancer learning combined with network propagation generalizes over multiple cancer types and identifies biologically plausible features that can serve as biomarkers for monitoring cancer survival.

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Highly performing cancer survival prediction with XGBoost

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Pan-cancer training outperforms single-cohort training

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Combined approach consisting of machine learning and network propagation

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Tumor microenvironment is most strongly involved in cancer survival prediction

MicroRNA Databases and Tools

In this era of big data, sets of methodologies and strategies are designed to extract knowledge from huge volumes of data. However, the cost of where and how to get this information accurately and quickly is extremely important, given the diversity of genomes and the different ways of representing that information. Among the huge set of information and relationships that the genome carries, there are sequences called miRNAs (microRNAs). These sequences were described in the 1990s and are mainly involved in mechanisms of regulation and gene expression. Having this in mind, this chapter focuses on exploring the available literature and providing useful and practical guidance on the miRNA database and tools topic. For that, we organized and present this text in two ways: (a) the update reviews and articles, which best summarize and discuss the theme; and (b) our update investigation on miRNA literature and portals about databases and tools. Finally, we present the main challenge and a possible solution to improve resources and tools.

New insights into the evolution of SPX gene family from algae to legumes; a focus on soybean

BACKGROUND

SPX-containing proteins have been known as key players in phosphate signaling and homeostasis. In Arabidopsis and rice, functions of some SPXs have been characterized, but little is known about their function in other plants, especially in the legumes.

RESULTS

We analyzed SPX gene family evolution in legumes and in a number of key species from algae to angiosperms. We found that SPX harboring proteins showed fluctuations in domain fusions from algae to the angiosperms with, finally, four classes appearing and being retained in the land plants. Despite these fluctuations, Lysine Surface Cluster (KSC), and the third residue of Phosphate Binding Sites (PBS) showed complete conservation in almost all of SPXs except few proteins in Selaginella moellendorffii and Papaver sumniferum, suggesting they might have different ligand preferences. In addition, we found that the WGD/segmentally or dispersed duplication types were the most frequent contributors to the SPX expansion, and that there is a positive correlation between the amount of WGD contribution to the SPX expansion in individual species and its number of EXS genes. We could also reveal that except SPX class genes, other classes lost the collinearity relationships among Arabidopsis and legume genomes. The sub- or neo-functionalization of the duplicated genes in the legumes makes it difficult to find the functional orthologous genes. Therefore, we used two different methods to identify functional orthologs in soybean and Medicago. High variance in the dynamic and spatial expression pattern of GmSPXs proved the new or sub-functionalization in the paralogs.

CONCLUSION

This comprehensive analysis revealed how SPX gene family evolved from algae to legumes and also discovered several new domains fused to SPX domain in algae. In addition, we hypothesized that there different phosphate sensing mechanisms might occur in S. moellendorffii and P. sumniferum. Finally, we predicted putative functional orthologs of AtSPXs in the legumes, especially, orthologs of AtPHO1, involved in long-distance Pi transportation. These findings help to understand evolution of phosphate signaling and might underpin development of new legume varieties with improved phosphate use efficiency.

ReGSP: a visualized application for homology-based gene searching and plotting using multiple reference sequences

The massively parallel nature of next-generation sequencing technologies has contributed to the generation of massive sequence data in the last two decades. Deciphering the meaning of each generated sequence requires multiple analysis tools, at all stages of analysis, from the reads stage all the way up to the whole-genome level. Homology-based approaches based on related reference sequences are usually the preferred option for gene and transcript prediction in newly sequenced genomes, resulting in the popularity of a variety of BLAST and BLAST-based tools. For organelle genomes, a single-reference-based gene finding tool that uses grouping parameters for BLAST results has been implemented in the Genome Search Plotter (GSP). However, this tool does not accept multiple and user-customized reference sequences required for a broad homology search. Here, we present multiple Reference-based Gene Search and Plot (ReGSP), a simple and convenient web tool that accepts multiple reference sequences for homology-based gene search. The tool incorporates cPlot, a novel dot plot tool, for illustrating nucleotide sequence similarity between the query and the reference sequences. ReGSP has an easy-to-use web interface and is freely accessible at https://ds.mju.ac.kr/regsp.

An accessible, efficient and global approach for the large-scale sequencing of bacterial genomes

We have developed an efficient and inexpensive pipeline for streamlining large-scale collection and genome sequencing of bacterial isolates. Evaluation of this method involved a worldwide research collaboration focused on the model organism Salmonella enterica, the 10KSG consortium. Following the optimization of a logistics pipeline that involved shipping isolates as thermolysates in ambient conditions, the project assembled a diverse collection of 10,419 isolates from low- and middle-income countries. The genomes were sequenced using the LITE pipeline for library construction, with a total reagent cost of less than USD$10 per genome. Our method can be applied to other large bacterial collections to underpin global collaborations.

Detection of low-level parental somatic mosaicism for clinically relevant SNVs and indels identified in a large exome sequencing dataset

BACKGROUND

Due to the limitations of the current routine diagnostic methods, low-level somatic mosaicism with variant allele fraction (VAF) < 10% is often undetected in clinical settings. To date, only a few studies have attempted to analyze tissue distribution of low-level parental mosaicism in a large clinical exome sequencing (ES) cohort.

METHODS

Using a customized bioinformatics pipeline, we analyzed apparent de novo single-nucleotide variants or indels identified in the affected probands in ES trio data at Baylor Genetics clinical laboratories. Clinically relevant variants with VAFs between 30 and 70% in probands and lower than 10% in one parent were studied. DNA samples extracted from saliva, buccal cells, redrawn peripheral blood, urine, hair follicles, and nail, representing all three germ layers, were tested using PCR amplicon next-generation sequencing (amplicon NGS) and droplet digital PCR (ddPCR).

RESULTS

In a cohort of 592 clinical ES trios, we found 61 trios, each with one parent suspected of low-level mosaicism. In 21 parents, the variants were validated using amplicon NGS and seven of them by ddPCR in peripheral blood DNA samples. The parental VAFs in blood samples varied between 0.08 and 9%. The distribution of VAFs in additional tissues ranged from 0.03% in hair follicles to 9% in re-drawn peripheral blood.

CONCLUSIONS

Our study illustrates the importance of analyzing ES data using sensitive computational and molecular methods for low-level parental somatic mosaicism for clinically relevant variants previously diagnosed in routine clinical diagnostics as apparent de novo.

Social stratification without genetic differentiation at the site of Kulubnarti in Christian Period Nubia

Relatively little is known about Nubia's genetic landscape prior to the influence of the Islamic migrations that began in the late 1st millennium CE. Here, we increase the number of ancient individuals with genome-level data from the Nile Valley from three to 69, reporting data for 66 individuals from two cemeteries at the Christian Period (~650-1000 CE) site of Kulubnarti, where multiple lines of evidence suggest social stratification. The Kulubnarti Nubians had ~43% Nilotic-related ancestry (individual variation between ~36-54%) with the remaining ancestry consistent with being  introduced through Egypt and ultimately deriving from an ancestry pool like that found in the Bronze and Iron Age Levant. The Kulubnarti gene pool - shaped over a millennium - harbors disproportionately female-associated West Eurasian-related ancestry. Genetic similarity among individuals from the two cemeteries supports a hypothesis of social division without genetic distinction. Seven pairs of inter-cemetery relatives suggest fluidity between cemetery groups. Present-day Nubians are not directly descended from the Kulubnarti Nubians, attesting to additional genetic input since the Christian Period.

Genetic determinants of endophytism in the Arabidopsis root mycobiome

The roots of Arabidopsis thaliana host diverse fungal communities that affect plant health and disease states. Here, we sequence the genomes of 41 fungal isolates representative of the A. thaliana root mycobiota for comparative analysis with other 79 plant-associated fungi. Our analyses indicate that root mycobiota members evolved from ancestors with diverse lifestyles and retain large repertoires of plant cell wall-degrading enzymes (PCWDEs) and effector-like small secreted proteins. We identify a set of 84 gene families associated with endophytism, including genes encoding PCWDEs acting on xylan (family GH10) and cellulose (family AA9). Transcripts encoding these enzymes are also part of a conserved transcriptional program activated by phylogenetically-distant mycobiota members upon host contact. Recolonization experiments with individual fungi indicate that strains with detrimental effects in mono-association with the host colonize roots more aggressively than those with beneficial activities, and dominate in natural root samples. Furthermore, we show that the pectin-degrading enzyme family PL1_7 links aggressiveness of endophytic colonization to plant health.

A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks

Identifying significantly mutated genes in cancer is essential for understanding the mechanisms of tumor initiation and progression. This task is a key challenge since large-scale genomic studies have reported an endless number of genes mutated at a shallow frequency. Towards uncovering infrequently mutated genes, gene interaction networks combined with mutation data have been explored. This work proposes Discovering Significant Cancer Genes (DiSCaGe), a computational method for discovering significant genes for cancer. DiSCaGe computes a mutation score for the genes based on the type of mutations they have. The influence received for their neighbors in the network is also considered and obtained through an asymmetric spreading strength applied to a consensus gene network. DiSCaGe produces a ranking of prioritized possible cancer genes. An experimental evaluation with six types of cancer revealed the potential of DiSCaGe for discovering known and possible novel significant cancer genes.

Wheat speciation and adaptation: perspectives from reticulate evolution

Reticulate evolution through the interchanging of genetic components across organisms can impact significantly on the fitness and adaptation of species. Bread wheat (Triticum aestivum subsp. aestivum) is one of the most important crops in the world. Allopolyploid speciation, frequent hybridization, extensive introgression, and occasional horizontal gene transfer (HGT) have been shaping a typical paradigm of reticulate evolution in bread wheat and its wild relatives, which is likely to have a substantial influence on phenotypic traits and environmental adaptability of bread wheat. In this review, we outlined the evolutionary history of bread wheat and its wild relatives with a highlight on the interspecific hybridization events, demonstrating the reticulate relationship between species/subspecies in the genera Triticum and Aegilops. Furthermore, we discussed the genetic mechanisms and evolutionary significance underlying the introgression of bread wheat and its wild relatives. An in-depth understanding of the evolutionary process of Triticum species should be beneficial to future genetic study and breeding of bread wheat.

Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations

BACKGROUND

With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA-disease associations (MDAs) is expensive and time-consuming. Practitioners have shown growing interest in methods for predicting potential MDAs. In recent years, an increasing number of computational methods for predicting novel MDAs have been developed, making a huge contribution to the research of human diseases and saving considerable time. In this paper, we proposed an efficient computational method, named bipartite graph-based collaborative matrix factorization (BGCMF), which is highly advantageous for predicting novel MDAs.

RESULTS

By combining two improved recommendation methods, a new model for predicting MDAs is generated. Based on the idea that some new miRNAs and diseases do not have any associations, we adopt the bipartite graph based on the collaborative matrix factorization method to complete the prediction. The BGCMF achieves a desirable result, with AUC of up to 0.9514 ± (0.0007) in the five-fold cross-validation experiments.

CONCLUSIONS

Five-fold cross-validation is used to evaluate the capabilities of our method. Simulation experiments are implemented to predict new MDAs. More importantly, the AUC value of our method is higher than those of some state-of-the-art methods. Finally, many associations between new miRNAs and new diseases are successfully predicted by performing simulation experiments, indicating that BGCMF is a useful method to predict more potential miRNAs with roles in various diseases.

Automatic consistency assurance for literature-based gene ontology annotation

BACKGROUND

Literature-based gene ontology (GO) annotation is a process where expert curators use uniform expressions to describe gene functions reported in research papers, creating computable representations of information about biological systems. Manual assurance of consistency between GO annotations and the associated evidence texts identified by expert curators is reliable but time-consuming, and is infeasible in the context of rapidly growing biological literature. A key challenge is maintaining consistency of existing GO annotations as new studies are published and the GO vocabulary is updated.

RESULTS

In this work, we introduce a formalisation of biological database annotation inconsistencies, identifying four distinct types of inconsistency. We propose a novel and efficient method using state-of-the-art text mining models to automatically distinguish between consistent GO annotation and the different types of inconsistent GO annotation. We evaluate this method using a synthetic dataset generated by directed manipulation of instances in an existing corpus, BC4GO. We provide detailed error analysis for demonstrating that the method achieves high precision on more confident predictions.

CONCLUSIONS

Two models built using our method for distinct annotation consistency identification tasks achieved high precision and were robust to updates in the GO vocabulary. Our approach demonstrates clear value for human-in-the-loop curation scenarios.

ConsensusPathDB 2022: molecular interactions update as a resource for network biology

Molecular interactions are key drivers of biological function. Providing interaction resources to the research community is important since they allow functional interpretation and network-based analysis of molecular data. ConsensusPathDB (http://consensuspathdb.org) is a meta-database combining interactions of diverse types from 31 public resources for humans, 16 for mice and 14 for yeasts. Using ConsensusPathDB, researchers commonly evaluate lists of genes, proteins and metabolites against sets of molecular interactions defined by pathways, Gene Ontology and network neighborhoods and retrieve complex molecular neighborhoods formed by heterogeneous interaction types. Furthermore, the integrated protein–protein interaction network is used as a basis for propagation methods. Here, we present the 2022 update of ConsensusPathDB, highlighting content growth, additional functionality and improved database stability. For example, the number of human molecular interactions increased to 859 848 connecting 200 499 unique physical entities such as genes/proteins, metabolites and drugs. Furthermore, we integrated regulatory datasets in the form of transcription factor–, microRNA– and enhancer–gene target interactions, thus providing novel functionality in the context of overrepresentation and enrichment analyses. We specifically emphasize the use of the integrated protein–protein interaction network as a scaffold for network inferences, present topological characteristics of the network and discuss strengths and shortcomings of such approaches.

Bioinformatic and cell-based tools for pooled CRISPR knockout screening in mosquitos

Mosquito-borne diseases present a worldwide public health burden. Current efforts to understand and counteract them have been aided by the use of cultured mosquito cells. Moreover, application in mammalian cells of forward genetic approaches such as CRISPR screens have identified essential genes and genes required for host-pathogen interactions, and in general, aided in functional annotation of genes. An equivalent approach for genetic screening of mosquito cell lines has been lacking. To develop such an approach, we design a new bioinformatic portal for sgRNA library design in several mosquito genomes, engineer mosquito cell lines to express Cas9 and accept sgRNA at scale, and identify optimal promoters for sgRNA expression in several mosquito species. We then optimize a recombination-mediated cassette exchange system to deliver CRISPR sgRNA and perform pooled CRISPR screens in an Anopheles cell line. Altogether, we provide a platform for high-throughput genome-scale screening in cell lines from disease vector species.

Two-stage-vote ensemble framework based on integration of mutation data and gene interaction network for uncovering driver genes

Identifying driver genes, exactly from massive genes with mutations, promotes accurate diagnosis and treatment of cancer. In recent years, a lot of works about uncovering driver genes based on integration of mutation data and gene interaction networks is gaining more attention. However, it is in suspense if it is more effective for prioritizing driver genes when integrating various types of mutation information (frequency and functional impact) and gene networks. Hence, we build a two-stage-vote ensemble framework based on somatic mutations and mutual interactions. Specifically, we first represent and combine various kinds of mutation information, which are propagated through networks by an improved iterative framework. The first vote is conducted on iteration results by voting methods, and the second vote is performed to get ensemble results of the first poll for the final driver gene list. Compared with four excellent previous approaches, our method has better performance in identifying driver genes on $33$ types of cancer from The Cancer Genome Atlas. Meanwhile, we also conduct a comparative analysis about two kinds of mutation information, five gene interaction networks and four voting strategies. Our framework offers a new view for data integration and promotes more latent cancer genes to be admitted.

Ionizing irradiation-induced Fgr in senescent cells mediates fibrosis

The role of cellular senescence in radiation-induced pulmonary fibrosis (RIPF) and the underlying mechanisms are unknown. We isolated radiation-induced senescent tdTOMp16 positive mesenchymal stem cells, established their absence of cell division, then measured levels of irradiation-induced expression of biomarkers of senescence by RNA-seq analysis. We identified a Log2 6.17-fold upregulation of tyrosine kinase Fgr, which was a potent inducer of biomarkers of fibrosis in target cells in non-contact co-cultures. Inhibition of Fgr by shRNA knockdown did not block radiation-induced senescence in vitro; however, both shRNA knockdown, or addition of a specific small-molecule inhibitor of Fgr, TL02-59, abrogated senescent cell induction of profibrotic genes in transwell-separated target cells. Single-cell RNA-seq (scRNAseq) analysis of mouse lungs at day 150 after 20 Gy thoracic irradiation revealed upregulation of Fgr in senescent neutrophils, and macrophages before detection of lung fibrosis. Thus, upregulated Fgr in radiation-induced senescent cells mediates RIPF and is a potential therapeutic target for the prevention of this radiation late effect.

Sampling the fish gill microbiome: a comparison of tissue biopsies and swabs

Background

Understanding the influence of methodology on results is an essential consideration in experimental design. In the expanding field of fish microbiology, many best practices and targeted techniques remain to be refined. This study aimed to compare microbial assemblages obtained from Atlantic salmon (Salmo salar) gills by swabbing versus biopsy excision. Results demonstrate the variation introduced by altered sampling strategies and enhance the available knowledge of the fish gill microbiome.

Results

The microbiome was sampled using swabs and biopsies from fish gills, with identical treatment of samples for 16S next generation Illumina sequencing. Results show a clear divergence in microbial communities obtained through the different sampling strategies, with swabbing consistently isolating a more diverse microbial consortia, and suffering less from the technical issue of host DNA contamination associated with biopsy use. Sequencing results from biopsy-derived extractions, however, hint at the potential for more cryptic localisation of some community members.

Conclusions

Overall, results demonstrate a divergence in the obtained microbial community when different sampling methodology is used. Swabbing appears a superior method for sampling the microbiota of mucosal surfaces for broad ecological research in fish, whilst biopsies might be best applied in exploration of communities beyond the reach of swabs, such as sub-surface and intracellular microbes, as well as in pathogen diagnosis. Most studies on the external microbial communities of aquatic organisms utilise swabbing for sample collection, likely due to convenience. Much of the ultrastructure of gill tissue in live fish is, however, potentially inaccessible to swabbing, meaning swabbing might fail to capture the full diversity of gill microbiota. This work therefore also provides valuable insight into partitioning of the gill microbiota, informing varied applications of different sampling methods in experimental design for future research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02374-0.

Transcriptome-wide N6-methyladenosine (m6A) methylation in watermelon under CGMMV infection

BACKGROUND

Cucumber green mottle mosaic virus (CGMMV) causes substantial global losses in cucurbit crops, especially watermelon. N6-methyladenosine (m⁶A) methylation in RNA is one of the most important post-transcriptional modification mechanisms in eukaryotes. It has been shown to have important regulatory functions in some model plants, but there has been no research regarding m⁶A modifications in watermelon.

RESULTS

We measured the global m⁶A level in resistant watermelon after CGMMV infection using a colorimetric method. And the results found that the global m⁶A level significantly decreased in resistant watermelon after CGMMV infection. Specifically, m⁶A libraries were constructed for the resistant watermelon leaves collected 48 h after CGMMV infection and the whole-genome m⁶A-seq were carried out. Numerous m⁶A modified peaks were identified from CGMMV-infected and control (uninfected) samples. The modification distributions and motifs of these m⁶A peaks were highly conserved in watermelon transcripts but the modification was more abundant than in other reported crop plants. In early response to CGMMV infection, 422 differentially methylated genes (DMGs) were identified, most of which were hypomethylated, and probably associated with the increased expression of watermelon m⁶A demethylase gene ClALKBH4B. Gene Ontology (GO) analysis indicated quite a few DMGs were involved in RNA biology and stress responsive pathways. Combined with RNA-seq analysis, there was generally a negative correlation between m⁶A RNA methylation and transcript level in the watermelon transcriptome. Both the m⁶A methylation and transcript levels of 59 modified genes significantly changed in response to CGMMV infection and some were involved in plant immunity.

CONCLUSIONS

Our study represents the first comprehensive characterization of m⁶A patterns in the watermelon transcriptome and helps to clarify the roles and regulatory mechanisms of m⁶A modification in watermelon in early responses to CGMMV.

Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen

A major drawback of single-cell ATAC-seq (scATAC-seq) is its sparsity, i.e., open chromatin regions with no reads due to loss of DNA material during the scATAC-seq protocol. Here, we propose scOpen, a computational method based on regularized non-negative matrix factorization for imputing and quantifying the open chromatin status of regulatory regions from sparse scATAC-seq experiments. We show that scOpen improves crucial downstream analysis steps of scATAC-seq data as clustering, visualization, cis-regulatory DNA interactions, and delineation of regulatory features. We demonstrate the power of scOpen to dissect regulatory changes in the development of fibrosis in the kidney. This identifies a role of Runx1 and target genes by promoting fibroblast to myofibroblast differentiation driving kidney fibrosis.

Deciphering the molecular mechanism of the cancer formation by chromosome structural dynamics

Cancer reflects the dysregulation of the underlying gene network, which is strongly related to the 3D genome organization. Numerous efforts have been spent on experimental characterizations of the structural alterations in cancer genomes. However, there is still a lack of genomic structural-level understanding of the temporal dynamics for cancer initiation and progression. Here, we use a landscape-switching model to investigate the chromosome structural transition during the cancerization and reversion processes. We find that the chromosome undergoes a non-monotonic structural shape-changing pathway with initial expansion followed by compaction during both of these processes. Furthermore, our analysis reveals that the chromosome with a more expanding structure than those at both the normal and cancer cell during cancerization exhibits a sparse contact pattern, which shows significant structural similarity to the one at the embryonic stem cell in many aspects, including the trend of contact probability declining with the genomic distance, the global structural shape geometry and the spatial distribution of loci on the chromosome. In light of the intimate structure-function relationship at the chromosomal level, we further describe the cell state transition processes by the chromosome structural changes, suggesting an elevated cell stemness during the formation of the cancer cells. We show that cell cancerization and reversion are highly irreversible processes in terms of the chromosome structural transition pathways, spatial repositioning of chromosomal loci and hysteresis loop of contact evolution analysis. Our model draws a molecular-scale picture of cell cancerization from the chromosome structural perspective. The process contains initial reprogramming towards the stem cell followed by the differentiation towards the cancer cell, accompanied by an initial increase and subsequent decrease of the cell stemness.