Bacteroides are predominant human colonic commensals, but the principal pathogenic species, Bacteroides fragilis (BF), lives closely associated with the mucosal surface, whereas a second major species, Bacteroides thetaiotaomicron (BT), concentrates within the colon. We find corresponding differences in their genomes, based on determination of the genome sequence of BF and comparative analysis with BT. Both species have acquired two mechanisms that contribute to their dominance among the colonic microbiota: an exceptional capability to use a wide range of dietary polysaccharides by gene amplification and the capacity to create variable surface antigenicities by multiple DNA inversion systems. However, the gene amplification for polysaccharide assimilation is more developed in BT, in keeping with its internal localization. In contrast, external antigenic structures can be changed more systematically in BF. Thereby, at the mucosal surface, where microbes encounter continuous attack by host defenses, BF evasion of the immune system is favored, and its colonization and infectious potential are increased. <<<

The Haemophilus cryptic genospecies is an important cause of maternal genital tract and neonatal systemic infections and initiates infection by colonizing the genital or respiratory epithelium. In recent work, we identified a unique Haemophilus cryptic genospecies protein called Cha, which mediates efficient adherence to genital and respiratory epithelia. The Cha adhesin belongs to the trimeric autotransporter family and contains an N-terminal signal peptide, an internal passenger domain that harbors adhesive activity, and a C-terminal membrane anchor domain. The passenger domain in Cha contains clusters of YadA-like head domains and neck motifs as well as a series of tandem 28-amino-acid peptide repeats. In the current study, we report that variation in peptide repeat number gradually modulates Cha adhesive activity, associated with a direct effect on the length of Cha fibers on the bacterial cell surface. The N-terminal 404 residues of the Cha passenger domain mediate binding to host cells and also facilitate bacterial aggregation through intermolecular Cha-Cha binding. As the tandem peptide repeats expand, the Cha fiber becomes longer and Cha adherence activity decreases. The expansion and contraction of peptide repeats represent a novel mechanism for modulating adhesive capacity, potentially balancing the need of the organism to colonize the genital and respiratory tracts with the ability to attach to alternative substrates, disperse within the host, or evade the host immune system. <<<

BACKGROUND: Helicobacter pylori is a pathogenic bacteria that colonize the gastrointestinal tract from human stomachs and causes diseases including gastritis, peptic ulcers, gastric lymphoma (MALT), and gastric cancer, with a higher prevalence in developing countries. Its high genetic diversity among strains is caused by a high mutation rate, observing virulence factors (VFs) variations in different geographic lineages. This study aimed to postulate the genetic variability associated with virulence factors present in the Helicobacter pylori strains, to identify the relationship of these genes with their phylogeographic origin.METHODS: The complete genomes of 135 strains available in NCBI, from different population origins, were analyzed using bioinformatics tools, identifying a high rate; as well as reorganization events in 87 virulence factor genes, divided into seven functional groups, to determine changes in position, number of copies, nucleotide identity and size, contrasting them with their geographical lineage and pathogenic phenotype.RESULTS: Bioinformatics analyses show a high rate of gene annotation errors in VF. Analysis of genetic variability of VFs shown that there is not a direct relationship between the reorganization and geographic lineage. However, regarding the pathogenic phenotype demonstrated in the analysis of many copies, size, and similarity when dividing the strains that possess and not the cag pathogenicity island (cagPAI), having a higher risk of developing gastritis and peptic ulcer was evidenced. Our data has shown that the analysis of the overall genetic variability of all VFs present in each strain of H. pylori is key information in understanding its pathogenic behavior. <<<

Pathogenic/likely pathogenic variants in susceptibility genes that interrupt RNA splicing are a well-documented mechanism of hereditary cancer syndromes development. However, if RNA studies are not performed, most of the variants beyond the canonical GT-AG splice site are characterized as variants of uncertain significance (VUS). To decrease the VUS burden, we have bioinformatically evaluated all novel VUS detected in 732 consecutive patients tested in the routine genetic counseling process. Twelve VUS that were predicted to cause splicing defects were selected for mRNA analysis. Here, we report a functional characterization of 12 variants located beyond the first two intronic nucleotides using RNAseq in , , , , , , , and genes. Based on the analysis of mRNA, we have successfully reclassified 50% of investigated variants. 25% of variants were downgraded to likely benign, whereas 25% were upgraded to likely pathogenic leading to improved clinical management of the patient and the family members. <<<

Andrea Degasperi, Xueqing Zou, Tauanne Dias Amarante, Andrea Martinez-Martinez, Gene Ching Chiek Koh, João M L Dias, Laura Heskin, Lucia Chmelova, Giuseppe Rinaldi, Valerie Ya Wen Wang, Arjun S Nanda, Aaron Bernstein, Sophie E Momen, Jamie Young, Daniel Perez-Gil, Yasin Memari, Cherif Badja, Scott Shooter, Jan Czarnecki, Matthew A Brown, Helen R Davies, Genomics England Research Consortium, Serena Nik-Zainal <<<

Whole-genome sequencing (WGS) permits comprehensive cancer genome analyses, revealing mutational signatures, imprints of DNA damage and repair processes that have arisen in each patient's cancer. We performed mutational signature analyses on 12,222 WGS tumor-normal matched pairs, from patients recruited via the UK National Health Service. We contrasted our results to two independent cancer WGS datasets, the International Cancer Genome Consortium (ICGC) and Hartwig Foundation, involving 18,640 WGS cancers in total. Our analyses add 40 single and 18 double substitution signatures to the current mutational signature tally. Critically, we show for each organ, that cancers have a limited number of 'common' signatures and a long tail of 'rare' signatures. We provide a practical solution for utilizing this concept of common versus rare signatures in future analyses. <<<

Varuni Sarwal, Sebastian Niehus, Ram Ayyala, Minyoung Kim, Aditya Sarkar, Sei Chang, Angela Lu, Neha Rajkumar, Nicholas Darfci-Maher, Russell Littman, Karishma Chhugani, Arda Soylev, Zoia Comarova, Emily Wesel, Jacqueline Castellanos, Rahul Chikka, Margaret G Distler, Eleazar Eskin, Jonathan Flint, Serghei Mangul <<<

Advances in whole-genome sequencing (WGS) promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from WGS data presents a substantial number of challenges and a plethora of SV detection methods have been developed. Currently, evidence that investigators can use to select appropriate SV detection tools is lacking. In this article, we have evaluated the performance of SV detection tools on mouse and human WGS data using a comprehensive polymerase chain reaction-confirmed gold standard set of SVs and the genome-in-a-bottle variant set, respectively. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of the SV detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance as the SV detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV detection tools varied widely in their performance, with several methods providing a good balance between sensitivity and precision. Additionally, we have determined the SV callers best suited for low- and ultralow-pass sequencing data as well as for different deletion length categories. <<<

N-methyladenine (mA) is the most abundant RNA modification in mammalian messenger RNAs (mRNAs), which participates in and regulates many important biological activities, such as tissue development and stem cell differentiation. Due to an improved understanding of mA, researchers have discovered that the biological function of mA can be linked to many stages of mRNA metabolism and that mA can regulate a variety of complex biological processes. In addition to its location on mammalian mRNAs, mA has been identified on viral transcripts. mA also plays important roles in the life cycle of many viruses and in viral replication in host cells. In this review, we briefly introduce the detection methods of mA, the mA-related proteins, and the functions of mA. We also summarize the effects of mA-related proteins on viral replication and infection. We hope that this review provides researchers with some insights for elucidating the complex mechanisms of the epitranscriptome related to viruses, and provides information for further study of the mechanisms of other modified nucleobases acting on processes such as viral replication. We also anticipate that this review can stimulate collaborative research from different fields, such as chemistry, biology, and medicine, and promote the development of antiviral drugs and vaccines. <<<

Bartonella henselae causes cat scratch disease and the vasculoproliferative disorders bacillary angiomatosis and peliosis hepatis in humans. One of the best known pathogenicity factors of B. henselae is Bartonella adhesin A (BadA), which is modularly constructed, consisting of head, neck/stalk, and membrane anchor domains. BadA is important for the adhesion of B. henselae to extracellular-matrix proteins and endothelial cells (ECs). In this study, we analyzed different B. henselae strains for BadA expression, autoagglutination, fibronectin (Fn) binding, and adhesion to ECs. We found that the B. henselae strains Marseille, ATCC 49882, Freiburg 96BK3 (FR96BK3), FR96BK38, and G-5436 express BadA. Remarkably, BadA expression was lacking in a B. henselae ATCC 49882 variant, in strains ATCC 49793 and Berlin-1, and in the majority of bacteria of strain Berlin-2. Adherence of B. henselae to ECs and Fn reliably correlated with BadA expression. badA was present in all tested strains, although the length of the gene varied significantly due to length variations of the stalk region. Sequencing of the promoter, head, and membrane anchor regions revealed only minor differences that did not correlate with BadA expression, apart from strain Berlin-1, in which a 1-bp deletion led to a frameshift in the head region of BadA. Our data suggest that, apart from the identified genetic modifications (frameshift deletion and recombination), other so-far-unknown regulatory mechanisms influence BadA expression. Because of variations between and within different B. henselae isolates, BadA expression should be analyzed before performing infection experiments with B. henselae. <<<

The Human Pangenome Reference Consortium (HPRC) presents a first draft human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals. These assemblies cover more than 99% of the expected sequence and are more than 99% accurate at the structural and base-pair levels. Based on alignments of the assemblies, we generated a draft pangenome that captures known variants and haplotypes, reveals novel alleles at structurally complex loci, and adds 119 million base pairs of euchromatic polymorphic sequence and 1,529 gene duplications relative to the existing reference, GRCh38. Roughly 90 million of the additional base pairs derive from structural variation. Using our draft pangenome to analyze short-read data reduces errors when discovering small variants by 34% and boosts the detected structural variants per haplotype by 104% compared to GRCh38-based workflows, and by 34% compared to using previous diversity sets of genome assemblies. <<<

Bo Zhou, Xuejiao Dou, Wei Wang, Hongxiang Yao, Feng Feng, Pan Wang, Zhengyi Yang, Ningyu An, Bing Liu, Xi Zhang, Yong Liu <<<

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia, and amnestic mild cognitive impairment (aMCI) has been defined as a transitional stage between normal aging and AD. Accumulating evidence has shown that altered functional connectivity (FC) and structural connectivity (SC) in the default mode network (DMN) is the prominent hallmarks of AD. However, the relationship between the changes in SC and FC of the DMN is not yet clear. In the present study, we derived the FC and SC matrices of the DMN with functional magnetic resonance imaging (fMRI) and diffusion-weighted imaging (DWI) data and further assessed FC and SC abnormalities within a discovery dataset of 120 participants (39 normal controls, 34 patients with aMCI and 47 patients with AD), as well as a replication dataset of 122 participants (43 normal controls, 37 patients with aMCI and 42 patients with AD). Disrupted SC and FC were found among DMN components (e.g., the posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), and hippocampus) in patients in the aMCI and AD groups in the discovery dataset; most of the disrupted connections were also identified in the replication dataset. More importantly, some SC and FC elements were significantly correlated with the cognitive ability of patients with aMCI and AD. In addition, we found structural-functional decoupling between the PCC and the right hippocampus in patients in the aMCI and AD groups. These findings of the alteration of DMN connectivity in neurodegenerative cohorts deepen our understanding of the pathophysiological mechanisms of AD. <<<

Omar Yossofzai, Aria Fallah, Cassia Maniquis, Shelly Wang, John Ragheb, Alexander G Weil, Tristan Brunette-Clement, Andrea Andrade, George M Ibrahim, Nicholas Mitsakakis, Elysa Widjaja <<<

OBJECTIVE: There is substantial variability in reported seizure outcome following pediatric epilepsy surgery, and lack of individualized predictive tools that could evaluate the probability of seizure freedom postsurgery. The aim of this study was to develop and validate a supervised machine learning (ML) model for predicting seizure freedom after pediatric epilepsy surgery.METHODS: This is a multicenter retrospective study of children who underwent epilepsy surgery at five pediatric epilepsy centers in North America. Clinical information, diagnostic investigations, and surgical characteristics were collected, and used as features to predict seizure-free outcome 1 year after surgery. The dataset was split randomly into 80% training and 20% testing data. Thirty-five combinations of five feature sets with seven ML classifiers were assessed on the training cohort using 10-fold cross-validation for model development. The performance of the optimal combination of ML classifier and feature set was evaluated in the testing cohort, and compared with logistic regression, a classical statistical approach.RESULTS: Of the 801 patients included, 61.3% were seizure-free 1 year postsurgery. During model development, the best combination was XGBoost ML algorithm with five features from the univariate feature set, including number of antiseizure medications, magnetic resonance imaging lesion, age at seizure onset, video-electroencephalography concordance, and surgery type, with a mean area under the curve (AUC) of .73 (95% confidence interval [CI] = .69-.77). The combination of XGBoost and univariate feature set was then evaluated on the testing cohort and achieved an AUC of .74 (95% CI = .66-.82; sensitivity = .87, 95% CI = .81-.94; specificity = .58, 95% CI = .47-.71). The XGBoost model outperformed the logistic regression model (AUC = .72, 95% CI = .63-.80; sensitivity = .72, 95% CI = .63-.82; specificity = .66, 95% CI = .53-.77) in the testing cohort (p = .005).SIGNIFICANCE: This study identified important features and validated an ML algorithm, XGBoost, for predicting the probability of seizure freedom after pediatric epilepsy surgery. Improved prognostication of epilepsy surgery is critical for presurgical counseling and will inform treatment decisions. <<<

C++ is a widely used programming language and the C++ front-end is a critical part of a C++ compiler. Although many techniques have been proposed to test compilers, few studies are devoted to detecting bugs in C++ compiler. In this study, we take the first step to detect bugs in C++ compiler front-ends. To do so, two main challenges need to be addressed, namely, the acquisition of test programs that are more likely to trigger bugs in compiler front-ends and the bug identification from complicated compiler outputs. In this article, we propose a novel framework named Ccoft to detect bugs in C++ compiler front-ends. To address the first challenge, Ccoft implements a practical program generator. The generator first transforms C++ grammars into a flexible structured format and then utilizes an equal-chance selection (ECS) strategy to conduct structure-aware grammar mutation to generate diverse C++ programs. Next, Ccoft employs a set of differential testing strategies to identify various kinds of bugs in C++ compiler front-ends by comparing complex outputs emitted by C++ compilers, thus tackling the second challenge. Empirical evaluation results over two mainstream compilers (i.e., GCC and Clang) show that Ccoft greatly improves two state-of-the-art approaches (i.e., Dharma and Grammarinator) by 135% and 111% in terms of the numbers of detected bugs, respectively. By running Ccoft for three months, we have successfully reported 136 bugs for two C++ compilers, of which 78 (57 confirmed, assigned, or fixed) for GCC and 58 (10 confirmed or fixed) for Clang. <<<

BACKGROUND: Immune checkpoint blockade (ICB) therapy has revolutionized the treatment of lung squamous cell carcinoma (LUSC). However, a significant proportion of patients with high tumour PD-L1 expression remain resistant to immune checkpoint inhibitors. To understand the underlying resistance mechanisms, characterization of the immunosuppressive tumour microenvironment and identification of biomarkers to predict resistance in patients are urgently needed.METHODS: Our study retrospectively analysed RNA sequencing data of 624 LUSC samples. We analysed gene expression patterns from tumour microenvironment by unsupervised clustering. We correlated the expression patterns with a set of T cell exhaustion signatures, immunosuppressive cells, clinical characteristics, and immunotherapeutic responses. Internal and external testing datasets were used to validate the presence of exhausted immune status.RESULTS: Approximately 28 to 36% of LUSC patients were found to exhibit significant enrichments of T cell exhaustion signatures, high fraction of immunosuppressive cells (M2 macrophage and CD4 Treg), co-upregulation of 9 inhibitory checkpoints (CTLA4, PDCD1, LAG3, BTLA, TIGIT, HAVCR2, IDO1, SIGLEC7, and VISTA), and enhanced expression of anti-inflammatory cytokines (e.g. TGFβ and CCL18). We defined this immunosuppressive group of patients as exhausted immune class (EIC). Although EIC showed a high density of tumour-infiltrating lymphocytes, these were associated with poor prognosis. EIC had relatively elevated PD-L1 expression, but showed potential resistance to ICB therapy. The signature of 167 genes for EIC prediction was significantly enriched in melanoma patients with ICB therapy resistance. EIC was characterized by a lower chromosomal alteration burden and a unique methylation pattern. We developed a web application ( http://lilab2.sysu.edu.cn/tex & http://liwzlab.cn/tex ) for researchers to further investigate potential association of ICB resistance based on our multi-omics analysis data.CONCLUSIONS: We introduced a novel LUSC immunosuppressive class which expressed high PD-L1 but showed potential resistance to ICB therapy. This comprehensive characterization of immunosuppressive tumour microenvironment in LUSC provided new insights for further exploration of resistance mechanisms and optimization of immunotherapy strategies. <<<

The rapid development of information technology has generated substantial data, which urgently requires new storage media and storage methods. DNA, as a storage medium with high density, high durability, and ultra-long storage time characteristics, is promising as a potential solution. However, DNA storage is still in its infancy and suffers from low space utilization of DNA strands, high read coverage, and poor coding coupling. Therefore, in this work, an adaptive coding DNA storage system is proposed to use different coding schemes for different coding region locations, and the method of adaptively generating coding constraint thresholds is used to optimize at the system level to ensure the efficient operation of each link. Images, videos, and PDF files of size 698 KB were stored in DNA using adaptive coding algorithms. The data were sequenced and losslessly decoded into raw data. Compared with previous work, the DNA storage system implemented by adaptive coding proposed in this paper has high storage density and low read coverage, which promotes the development of carbon-based storage systems. <<<

There are major efforts underway to make genome sequencing a routine part of clinical practice. A critical barrier to these is achieving practical solutions for data ownership and integrity. Blockchain provides solutions to these challenges in other realms, such as finance. However, its use in genomics is stymied due to the difficulty in storing large-scale data on-chain, slow transaction speeds, and limitations on querying. To overcome these roadblocks, we developed a private blockchain network to store genomic variants and reference-aligned reads on-chain. It uses nested database indexing with an accompanying tool suite to rapidly access and analyze the data. <<<

Investigating the functions and activities of genes requires proper annotation of the transcribed units. However, transcript assembly efforts have produced a surprisingly large variation in the number of transcripts, and especially so for noncoding transcripts. This heterogeneity in assembled transcript sets might be partially explained by sequencing depth. Here, we used real and simulated short-read sequencing data as well as long-read data to systematically investigate the impact of sequencing depths on the accuracy of assembled transcripts. We assembled and analyzed transcripts from 671 human short-read data sets and four long-read data sets. At the first level, there is a positive correlation between the number of reads and the number of recovered transcripts. However, the effect of the sequencing depth varied based on cell or tissue type, the type of read and the nature and expression levels of the transcripts. The detection of coding transcripts saturated rapidly with both short and long-reads, however, there was no sign of early saturation for noncoding transcripts at any sequencing depth. Increasing long-read sequencing depth specifically benefited transcripts containing transposable elements. Finally, we show how single-cell RNA-seq can be guided by transcripts assembled from bulk long-read samples, and demonstrate that noncoding transcripts are expressed at similar levels to coding transcripts but are expressed in fewer cells. This study highlights the impact of sequencing depth on transcript assembly. <<<

Our representation of the physical world requires judgments of magnitudes, such as loudness, distance, or time. Interestingly, magnitude estimates are often not veridical but subject to characteristic biases. These biases are strikingly similar across different sensory modalities, suggesting common processing mechanisms that are shared by different sensory systems. However, the search for universal neurobiological principles of magnitude judgments requires guidance by formal theories. Here, we discuss a unifying Bayesian framework for understanding biases in magnitude estimation. This Bayesian perspective enables a re-interpretation of a range of established psychophysical findings, reconciles seemingly incompatible classical views on magnitude estimation, and can guide future investigations of magnitude estimation and its neurobiological mechanisms in health and in psychiatric diseases, such as schizophrenia. <<<

Bipolar disorder (BIP) is one of the most common hereditary psychiatric disorders worldwide. Elucidating the genetic basis of BIP will play a pivotal role in mechanistic delineation. Genome-wide association studies (GWAS) have successfully reported multiple susceptibility loci conferring BIP risk, thus providing insight into the effects of its underlying pathobiology. However, difficulties remain in the extrication of important and biologically relevant data from genetic discoveries related to psychiatric disorders such as BIP. There is an urgent need for an integrated and comprehensive online database with unified access to genetic and multi-omics data for in-depth data mining. Here, we developed the dbBIP, a database for BIP genetic research based on published data. The dbBIP consists of several modules, i.e.: (i) single nucleotide polymorphism (SNP) module, containing large-scale GWAS genetic summary statistics and functional annotation information relevant to risk variants; (ii) gene module, containing BIP-related candidate risk genes from various sources and (iii) analysis module, providing a simple and user-friendly interface to analyze one's own data. We also conducted extensive analyses, including functional SNP annotation, integration (including summary-data-based Mendelian randomization and transcriptome-wide association studies), co-expression, gene expression, tissue expression, protein-protein interaction and brain expression quantitative trait loci analyses, thus shedding light on the genetic causes of BIP. Finally, we developed a graphical browser with powerful search tools to facilitate data navigation and access. The dbBIP provides a comprehensive resource for BIP genetic research as well as an integrated analysis platform for researchers and can be accessed online at http://dbbip.xialab.info. Database URL: http://dbbip.xialab.info. <<<

Long diagnostic wait times hinder international efforts to address antibiotic resistance in M. tuberculosis. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution. However, generalizability and clinical adoption have been limited by a lack of interpretability, especially in deep learning methods. Here, we present two deep convolutional neural networks that predict antibiotic resistance phenotypes of M. tuberculosis isolates: a multi-drug CNN (MD-CNN), that predicts resistance to 13 antibiotics based on 18 genomic loci, with AUCs 82.6-99.5% and higher sensitivity than state-of-the-art methods; and a set of 13 single-drug CNNs (SD-CNN) with AUCs 80.1-97.1% and higher specificity than the previous state-of-the-art. Using saliency methods to evaluate the contribution of input sequence features to the SD-CNN predictions, we identify 18 sites in the genome not previously associated with resistance. The CNN models permit functional variant discovery, biologically meaningful interpretation, and clinical applicability. <<<

Alport syndrome is an inherited disorder of the kidneys that results from variants in three collagen IV genes-COL4A3, COL4A4, and COL4A5. Early diagnosis and pharmacologic intervention can delay the progression of chronic kidney disease and the onset of kidney failure in patients with Alport syndrome. This article describes the evolution of approaches to the diagnosis and early treatment of Alport syndrome. <<<