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Abstract Background COVID-19 can cause cardiac complications and the latter are associated with poor prognosis and increased mortality. SARS-CoV-2 variants differ in their infectivity and pathogenicity, but how they affect cardiomyocytes (CMs) is unclear. Methods The effects of SARS-CoV-2 variants were investigated using human induced pluripotent stem cell-derived (hiPSC-) CMs in vitro and Golden Syrian hamsters in vivo. Results Different variants exhibited distinct tropism, mechanism of viral entry and pathology in the heart. Omicron BA.2 most efficiently infected and injured CMs in vitro and in vivo, and induced expression changes consistent with increased cardiac dysfunction, compared to other variants tested. Bioinformatics and upstream regulator analyses identified transcription factors and network predicted to control the unique transcriptome of Omicron BA.2 infected CMs. Increased infectivity of Omicron BA.2 is attributed to its ability to infect via endocytosis, independently of TMPRSS2, which is absent in CMs. Conclusions In this study, we reveal previously unknown differences in how different SARS-CoV-2 variants affect CMs. Omicron BA.2, which is generally thought to cause mild disease, can damage CMs in vitro and in vivo. Our study highlights the need for further investigations to define the pathogenesis of cardiac complications arising from different SARS-CoV-2 variants.

Abstract Objectives The clinical decision-making regarding choosing surgery alone (SA) or surgery followed by postoperative adjuvant chemotherapy (SPOCT) in esophageal squamous cell carcinoma (ESCC) remains controversial. We aim to propose a pre-therapy PET/CT image-based deep learning approach to improve the survival benefit and clinical management of ESCC patients. Methods This retrospective multicenter study included 837 ESCC patients from three institutions. Prognostic biomarkers integrating six networks were developed to build an ESCC prognosis (ESCCPro) model and predict the survival probability of ESCC patients treated with SA and SPOCT. Patients who did not undergo surgical resection were in a control group. Overall survival (OS) was the primary end-point event. The expected improvement in survival prognosis with the application of ESCCPro to assign treatment protocols was estimated by comparing the survival of patients in each subgroup. Seven clinicians with varying experience evaluated how ESCCPro performed in assisting clinical decision-making. Results In this retrospective multicenter study, patients receiving SA had a median OS 9.2 months longer than controls. No significant differences in survival were found between SA patients with predicted poor outcomes and the controls (p > 0.05). It was estimated that if ESCCPro was used to determine SA and SPOCT eligibility, the median OS in the ESCCPro-recommended SA group and SPOCT group would have been 15.3 months and 24.9 months longer, respectively. In addition, ESCCPro also significantly improved prognosis accuracy, certainty, and the efficiency of clinical experts. Conclusion ESCCPro assistance improved the survival benefit of ESCC patients and the clinical decision-making among the two treatment approaches. Critical relevance statement The ESCCPro model for treatment decision-making is promising to improve overall survival in ESCC patients undergoing surgical resection and patients undergoing surgery followed by postoperative adjuvant chemotherapy. Key Points ESCC is associated with a poor prognosis and unclear ideal treatments. ESCCPro predicts the survival of patients with ESCC and the expected benefit from SA. ESCCPro improves clinicians’ stratification of patients’ prognoses. Graphical Abstract

Abstract Background Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing. Results In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30–50 aa; 100–150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG. Conclusions ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract

Abstract Background Despite the existence of proposed prognostic features on computed tomography (CT) for patients with advanced‐stage non‐small cell lung cancer (NSCLC), including radiologists' handcrafted (RaH) features, radiomics features, and deep learning features, comprehensive studies that examine their reproducibility, stability, and biological interpretability remain limited. Methods The Image Biomarker Standardization Initiative‐reported tolerance, Kappa, interclass correlation coefficient, and coefficient of variance were employed to identify reproducible features among RaH, radiomics, and deep learning features derived from NSCLC phantoms. The reproducible features were then input into six artificial intelligence algorithms to develop prognostic models for targeted therapy and immunotherapy using real‐world patients with advanced‐stage NSCLC to assess their capability and stability. Pathway enrichment was also conducted to explore the underlying biological pathways associated with these reproducible features. Results Reproducible features in advanced NSCLC included RaH features (9/9, 100%), radiomics features (572/1835, 31.17%), and deep learning features (3442/4096, 84.03%). Among the six artificial intelligence‐based prognostic methods, the RaH features exhibited least variability. We also observed that the optimal CT‐based prognostic approach differed depending on treatment regimens for advanced NSCLC. In analysis using the Cancer Genome Atlas Program lung adenocarcinoma dataset, the identified reproducible prognostic features, specifically tumor size‐derived radiomics and RaH features, showed significant associations with five key signaling pathways involved in NSCLC survival outcomes (false‐discovery rate p

Background & Aims: Inflammatory bowel disease (IBD) is commonly associated with extraintestinal complications, including autoimmune liver disease. The co-occurrence of IBD and primary biliary cholangitis (PBC) has been increasingly observed, but the underlying relationship between these conditions remains unclear. Methods: Using summary statistics from genome-wide association studies (GWAS), we investigated the causal effects between PBC and IBD, including Crohn’s disease (CD) and ulcerative colitis (UC). We also analyzed the shared genetic architecture between IBD and PBC using data from GWAS, bulk-tissue RNA sequencing, and single cell RNA sequencing, and explored potential functional genes. Result: There was a strong positive genetic correlation between PBC and IBD (linkage disequilibrium score regression: rg = 0.2249, p = 3.38 × 10-5). Cross-trait analysis yielded 10 shared-risk single nucleotide polymorphisms (SNPs), as well as nine novel SNPs, which were associated with both traits. Using Mendelian randomization, a stable causal effect was established of PBC on IBD. Genetically predicted PBC was found to have a risk effect on IBD (1.105; 95% CI: 1.058–1.15; p = 1.16 × 10-10), but not vice versa. Shared tissue-specific heritability enrichment was identified for PBC and IBD (including CD and UC) in lung, spleen, and whole-blood samples. Furthermore, shared enrichment was observed of specific cell types (T cells, B cells, and natural killer cells) and their subtypes. Nine functional genes were identified based on summary statistics-based Mendelian randomization. Conclusions: This study detected shared genetic architecture between IBD and PBC and demonstrated a stable causal relationship of genetically predicted PBC on the risk of IBD. These findings shed light on the biological basis of comorbidity between IBD and PBC, and have important implications for intervention and treatment targets of these two diseases simultaneously. Impact and Implications: The discovery of novel shared single nucleotide polymorphisms (SNPs) and functional genes provides insights into the common targets between inflammatory bowel disease (IBD) and primary biliary cholangitis (PBC), serving as a basis for new drug development and contributing to the study of disease pathogenesis. Additionally, the established significant causality and genetic correlation underscore the importance of clinical intervention in preventing the comorbidity of IBD and PBC. The enrichment of SNP heritability in specific tissues and cell types reveals the role of immune factors in the potential disease mechanisms shared between IBD and PBC. This stimulates further research on potential interventions and could lead to the development of new targets for immune-based therapies.

Abstract Background Emerging evidence suggests that Rho GTPases play a crucial role in tumorigenesis and metastasis, but their involvement in the tumor microenvironment (TME) and prognosis of hepatocellular carcinoma (HCC) is not well understood. Methods We aim to develop a tumor prognosis prediction system called the Rho GTPases-related gene score (RGPRG score) using Rho GTPase signaling genes and further bioinformatic analyses. Results Our work found that HCC patients with a high RGPRG score had significantly worse survival and increased immunosuppressive cell fractions compared to those with a low RGPRG score. Single-cell cohort analysis revealed an immune-active TME in patients with a low RGPRG score, with strengthened communication from T/NK cells to other cells through MIF signaling networks. Targeting these alterations in TME, the patients with high RGPRG score have worse immunotherapeutic outcomes and decreased survival time in the immunotherapy cohort. Moreover, the RGPRG score was found to be correlated with survival in 27 other cancers. In vitro experiments confirmed that knockdown of the key Rho GTPase-signaling biomarker SFN significantly inhibited HCC cell proliferation, invasion, and migration. Conclusions This study provides new insight into the TME features and clinical use of Rho GTPase gene pattern at the bulk-seq and single-cell level, which may contribute to guiding personalized treatment and improving clinical outcome in HCC.

Abstract Aims Dissecting complex interactions among transcription factors (TFs), microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) are central for understanding heart development and function. Although computational approaches and platforms have been described to infer relationships among regulatory factors and genes, current approaches do not adequately account for how highly diverse, interacting regulators that include noncoding RNAs (ncRNAs) control cardiac gene expression dynamics over time. Methods To overcome this limitation, we devised an integrated framework, cardiac gene regulatory modeling (CGRM) that integrates LogicTRN and regulatory component analysis bioinformatics modeling platforms to infer complex regulatory mechanisms. We then used CGRM to identify and compare the TF-ncRNA gene regulatory networks that govern early- and late-stage cardiomyocytes (CMs) generated by in vitro differentiation of human pluripotent stem cells (hPSC) and ventricular and atrial CMs isolated during in vivo human cardiac development. Results Comparisons of in vitro versus in vivo derived CMs revealed conserved regulatory networks among TFs and ncRNAs in early cells that significantly diverged in late staged cells. We report that cardiac genes (“heart targets”) expressed in early-stage hPSC-CMs are primarily regulated by MESP1, miR-1, miR-23, lncRNAs NEAT1 and MALAT1, while GATA6, HAND2, miR-200c, NEAT1 and MALAT1 are critical for late hPSC-CMs. The inferred TF-miRNA-lncRNA networks regulating heart development and contraction were similar among early-stage CMs, among individual hPSC-CM datasets and between in vitro and in vivo samples. However, genes related to apoptosis, cell cycle and proliferation, and transmembrane transport showed a high degree of divergence between in vitro and in vivo derived late-stage CMs. Overall, late-, but not early-stage CMs diverged greatly in the expression of “heart target” transcripts and their regulatory mechanisms. Conclusions In conclusion, we find that hPSC-CMs are regulated in a cell autonomous manner during early development that diverges significantly as a function of time when compared to in vivo derived CMs. These findings demonstrate the feasibility of using CGRM to reveal dynamic and complex transcriptional and posttranscriptional regulatory interactions that underlie cell directed versus environment-dependent CM development. These results with in vitro versus in vivo derived CMs thus establish this approach for detailed analyses of heart disease and for the analysis of cell regulatory systems in other biomedical fields.

Abstract Background With the continuous advances in third-generation sequencing technology and the increasing affordability of next-generation sequencing technology, sequencing data from different sequencing technology platforms is becoming more common. While numerous benchmarking studies have been conducted to compare variant-calling performance across different platforms and approaches, little attention has been paid to the potential of leveraging the strengths of different platforms to optimize overall performance, especially integrating Oxford Nanopore and Illumina sequencing data. Results We investigated the impact of multi-platform data on the performance of variant calling through carefully designed experiments with a deep learning-based variant caller named Clair3-MP (Multi-Platform). Through our research, we not only demonstrated the capability of ONT-Illumina data for improved variant calling, but also identified the optimal scenarios for utilizing ONT-Illumina data. In addition, we revealed that the improvement in variant calling using ONT-Illumina data comes from an improvement in difficult genomic regions, such as the large low-complexity regions and segmental and collapse duplication regions. Moreover, Clair3-MP can incorporate reference genome stratification information to achieve a small but measurable improvement in variant calling. Clair3-MP is accessible as an open-source project at: https://github.com/HKU-BAL/Clair3-MP . Conclusions These insights have important implications for researchers and practitioners alike, providing valuable guidance for improving the reliability and efficiency of genomic analysis in diverse applications.

Abstract Sensitive detection of Mycobacterium tuberculosis (TB) in small percentages in metagenomic samples is essential for microbial classification and drug resistance prediction. However, traditional methods, such as bacterial culture and microscopy, are time-consuming and sometimes have limited TB detection sensitivity. Oxford nanopore technologies (ONT) MinION sequencing allows rapid and simple sample preparation for sequencing. Its recently developed adaptive sequencing selects reads from targets while allowing real-time base-calling to achieve sequence enrichment or depletion during sequencing. Another common enrichment method is PCR amplification of the target TB genes. In this study, we compared both methods using ONT MinION sequencing for TB detection and variant calling in metagenomic samples using both simulation runs and those with synthetic and patient samples. We found that both methods effectively enrich TB reads from a high percentage of human (95%) and other microbial DNA. Adaptive sequencing with readfish and UNCALLDE achieved a 3.9-fold and 2.2-fold enrichment compared to the control run. We provide a simple automatic analysis framework to support the detection of TB for clinical use, openly available at https://github.com/HKU-BAL/ONT-TB-NF . Depending on the patient's medical condition and sample type, we recommend users evaluate and optimize their workflow for different clinical specimens to improve the detection limit.

Abstract Background Very low-coverage (0.1 to 1×) whole genome sequencing (WGS) has become a promising and affordable approach to discover genomic variants of human populations for genome-wide association study (GWAS). To support genetic screening using preimplantation genetic testing (PGT) in a large population, the sequencing coverage goes below 0.1× to an ultra-low level. However, the feasibility and effectiveness of ultra-low-coverage WGS (ulcWGS) for GWAS remains undetermined. Methods We built a pipeline to carry out analysis of ulcWGS data for GWAS. To examine its effectiveness, we benchmarked the accuracy of genotype imputation at the combination of different coverages below 0.1× and sample sizes from 2000 to 16,000, using 17,844 embryo PGT samples with approximately 0.04× average coverage and the standard Chinese sample HG005 with known genotypes. We then applied the imputed genotypes of 1744 transferred embryos who have gestational ages and complete follow-up records to GWAS. Results The accuracy of genotype imputation under ultra-low coverage can be improved by increasing the sample size and applying a set of filters. From 1744 born embryos, we identified 11 genomic risk loci associated with gestational ages and 166 genes mapped to these loci according to positional, expression quantitative trait locus, and chromatin interaction strategies. Among these mapped genes, CRHBP, ICAM1, and OXTR were more frequently reported as preterm birth related. By joint analysis of gene expression data from previous studies, we constructed interrelationships of mainly CRHBP, ICAM1, PLAGL1, DNMT1, CNTLN, DKK1, and EGR2 with preterm birth, infant disease, and breast cancer. Conclusions This study not only demonstrates that ulcWGS could achieve relatively high accuracy of adequate genotype imputation and is capable of GWAS, but also provides insights into the associations between gestational age and genetic variations of the fetal embryos from Chinese population.

Abstract Background Whole genome sequencing using the long-read Oxford Nanopore Technologies (ONT) MinION sequencer provides a cost-effective option for structural variant (SV) detection in clinical applications. Despite the advantage of using long reads, however, accurate SV calling and phasing are still challenging. Results We introduce Duet, an SV detection tool optimized for SV calling and phasing using ONT data. The tool uses novel features integrated from both SV signatures and single-nucleotide polymorphism signatures, which can accurately distinguish SV haplotype from a false signal. Duet was benchmarked against state-of-the-art tools on multiple ONT sequencing datasets of sequencing coverage ranging from 8× to 40×. At low sequencing coverage of 8×, Duet performs better than all other tools in SV calling, SV genotyping and SV phasing. When the sequencing coverage is higher (20× to 40×), the F1-score for SV phasing is further improved in comparison to the performance of other tools, while its performance of SV genotyping and SV calling remains higher than other tools. Conclusion Duet can perform accurate SV calling, SV genotyping and SV phasing using low-coverage ONT data, making it very useful for low-coverage genomes. It has great performance when scaled to high-coverage genomes, which is adaptable to various clinical applications. Duet is open source and is available at https://github.com/yekaizhou/duet .

Abstract Background The application of long-read sequencing using the Oxford Nanopore Technologies (ONT) MinION sequencer is getting more diverse in the medical field. Having a high sequencing error of ONT and limited throughput from a single MinION flowcell, however, limits its applicability for accurate variant detection. Medical exome sequencing (MES) targets clinically significant exon regions, allowing rapid and comprehensive screening of pathogenic variants. By applying MES with MinION sequencing, the technology can achieve a more uniform capture of the target regions, shorter turnaround time, and lower sequencing cost per sample. Method We introduced a cost-effective optimized workflow, ECNano, comprising a wet-lab protocol and bioinformatics analysis, for accurate variant detection at 4800 clinically important genes and regions using a single MinION flowcell. The ECNano wet-lab protocol was optimized to perform long-read target enrichment and ONT library preparation to stably generate high-quality MES data with adequate coverage. The subsequent variant-calling workflow, Clair-ensemble, adopted a fast RNN-based variant caller, Clair, and was optimized for target enrichment data. To evaluate its performance and practicality, ECNano was tested on both reference DNA samples and patient samples. Results ECNano achieved deep on-target depth of coverage (DoC) at average > 100× and > 98% uniformity using one MinION flowcell. For accurate ONT variant calling, the generated reads sufficiently covered 98.9% of pathogenic positions listed in ClinVar, with 98.96% having at least 30× DoC. ECNano obtained an average read length of 1000 bp. The long reads of ECNano also covered the adjacent splice sites well, with 98.5% of positions having ≥ 30× DoC. Clair-ensemble achieved > 99% recall and accuracy for SNV calling. The whole workflow from wet-lab protocol to variant detection was completed within three days. Conclusion We presented ECNano, an out-of-the-box workflow comprising (1) a wet-lab protocol for ONT target enrichment sequencing and (2) a downstream variant detection workflow, Clair-ensemble. The workflow is cost-effective, with a short turnaround time for high accuracy variant calling in 4800 clinically significant genes and regions using a single MinION flowcell. The long-read exon captured data has potential for further development, promoting the application of long-read sequencing in personalized disease treatment and risk prediction.

Abstract Structural variation (SV) is a major cause of genetic disorders. In this paper, we show that low-depth (specifically, 4×) whole-genome sequencing using a single Oxford Nanopore MinION flow cell suffices to support sensitive detection of SV, particularly pathogenic SV for supporting clinical diagnosis. When using 4× ONT WGS data, existing SV calling software often fails to detect pathogenic SV, especially in the form of long deletion, terminal deletion, duplication, and unbalanced translocation. Our new SV calling software SENSV can achieve high sensitivity for all types of SV and a breakpoint precision typically ± 100 bp; both features are important for clinical concerns. The improvement achieved by SENSV stems from several new algorithms. We evaluated SENSV and other software using both real and simulated data. The former was based on 24 patient samples, each diagnosed with a genetic disorder. SENSV found the pathogenic SV in 22 out of 24 cases (all heterozygous, size from hundreds of kbp to a few Mbp), reporting breakpoints within 100 bp of the true answers. On the other hand, no existing software can detect the pathogenic SV in more than 10 out of 24 cases, even when the breakpoint requirement is relaxed to ± 2000 bp.

Qiuhui Li et al. report a pan-genome sequence study in Han Chinese genomes. They find 276 Mbp of DNA sequences (absent in the current human reference), and report that 46.646 Mbp common sequences improved the accuracy of variant calling and mapping rate when added to the reference genome.

ABSTRACT Pet bite-related infections are commonly caused by the pet’s oral flora transmitted to the animal handlers through the bite wounds. In this study, we isolated a streptococcus, HKU75T, in pure culture from the purulent discharge collected from a guinea pig bite wound in a previously healthy young patient. HKU75T was alpha-hemolytic on sheep blood agar and agglutinated with Lancefield group D and group G antisera. API 20 STREP showed that the most likely identity for HKU75T was S. suis I with 85.4% confidence while Vitek 2 showed that HKU75T was unidentifiable. MALDI-TOF MS identified HKU75T as Streptococcus suis (score of 1.86 only). 16S rRNA gene sequencing showed that HKU75T was most closely related to S. parasuis (98.3% nucleotide identity), whereas partial groEL and rpoB gene sequencing showed that it was most closely related to S. suis (81.8% and 89.8% nucleotide identity respectively). Whole genome sequencing and intergenomic distance determined by ANI revealed that there was

Abstract Background Next-generation sequencing (NGS) enables unbiased detection of pathogens by mapping the sequencing reads of a patient sample to the known reference sequence of bacteria and viruses. However, for a new pathogen without a reference sequence of a close relative, or with a high load of mutations compared to its predecessors, read mapping fails due to a low similarity between the pathogen and reference sequence, which in turn leads to insensitive and inaccurate pathogen detection outcomes. Results We developed MegaPath, which runs fast and provides high sensitivity in detecting new pathogens. In MegaPath, we have implemented and tested a combination of polishing techniques to remove non-informative human reads and spurious alignments. MegaPath applies a global optimization to the read alignments and reassigns the reads incorrectly aligned to multiple species to a unique species. The reassignment not only significantly increased the number of reads aligned to distant pathogens, but also significantly reduced incorrect alignments. MegaPath implements an enhanced maximum-exact-match prefix seeding strategy and a SIMD-accelerated Smith-Waterman algorithm to run fast. Conclusions In our benchmarks, MegaPath demonstrated superior sensitivity by detecting eight times more reads from a low-similarity pathogen than other tools. Meanwhile, MegaPath ran much faster than the other state-of-the-art alignment-based pathogen detection tools (and compariable with the less sensitivity profile-based pathogen detection tools). The running time of MegaPath is about 20 min on a typical 1 Gb dataset.

Abstract Background Genome-centric approaches are widely used to investigate microbial compositions, dynamics, ecology, and interactions within various environmental systems. Hundreds or even thousands of genomes could be retrieved in a single study contributed by the cost-effective short-read sequencing and developed assembly/binning pipelines. However, conventional binning methods usually yield highly fragmented draft genomes that limit our ability to comprehensively understand these microbial communities. Thus, to leverage advantage of both the long and short reads to retrieve more complete genomes from environmental samples is a must-do task to move this direction forward. Results Here, we used an iterative hybrid assembly (IHA) approach to reconstruct 49 metagenome-assembled genomes (MAGs), including 27 high-quality (HQ) and high-contiguity (HC) genomes with contig number ≤ 5, eight of which were circular finished genomes from a partial-nitritation anammox (PNA) reactor. These 49 recovered MAGs (43 MAGs encoding full-length rRNA, average N50 of 2.2 Mbp), represented the majority (92.3%) of the bacterial community. Moreover, the workflow retrieved HQ and HC MAGs even with an extremely low coverage (relative abundance < 0.1%). Among them, 34 MAGs could not be assigned to the genus level, indicating the novelty of the genomes retrieved using the IHA method proposed in this study. Comparative analysis of HQ MAG pairs reconstructed using two methods, i.e., hybrid and short reads only, revealed that identical genes in the MAG pairs represented 87.5% and 95.5% of the total gene inventory of hybrid and short reads only assembled MAGs, respectively. In addition, the first finished anammox genome of the genus Ca. Brocadia reconstructed revealed that there were two identical hydrazine synthase (hzs) genes, providing the exact gene copy number of this crucial phylomarker of anammox at the genome level. Conclusions Our results showcased the high-quality and high-contiguity genome retrieval performance and demonstrated the feasibility of complete genome reconstruction using the IHA workflow from the enrichment system. These (near-) complete genomes provided a high resolution of the microbial community, which might help to understand the bacterial repertoire of anammox-associated systems. Combined with other validation experiments, the workflow can enable a detailed view of the anammox or other similar enrichment systems. Video Abstract

Inefficient differentiation and insufficient maturation are barriers to the application of human pluripotent stem cell (hPSC)-derived cardiomyocytes (CMs) for research and therapy. Great strides have been made to the former, and multiple groups have reported cardiac differentiation protocol that can generate hPSC-CMs at high efficiency. Although many such protocols are based on the modulation of the WNT signaling pathway, they differ in their timing and in the WNT inhibitors used. Little is currently known about whether and how conditions of differentiation affect cardiac maturation. Here we adapted multiple cardiac differentiation protocols to improve cost-effectiveness and consistency, and compared the properties of the hPSC-CMs generated. Our results showed that the schedule of differentiation, but not the choice of WNT inhibitors, was a critical determinant not only of differentiation efficiency, which was expected, but also CM maturation. Among cultures with comparable purity, hPSC-CMs generated with different differentiation schedules vary in the expression of genes which are important for developmental maturation, and in their structural, metabolic, calcium transient and proliferative properties. In summary, we demonstrated that simple changes in the schedule of cardiac differentiation could promote maturation. To this end, we have optimized a cardiac differentiation protocol that can simultaneously achieve high differentiation efficiency and enhanced developmental maturation. Our findings would advance the production of hPSC-CMs for research and therapy.

Summary: The Human Genome Project opened an era of (epi)genomic research, and also provided a platform for the development of new sequencing technologies. During and after the project, several sequencing technologies continue to dominate nucleic acid sequencing markets. Currently, Illumina (short-read), PacBio (long-read), and Oxford Nanopore (long-read) are the most popular sequencing technologies. Unlike PacBio or the popular short-read sequencers before it, which, as examples of the second or so-called Next-Generation Sequencing platforms, need to synthesize when sequencing, nanopore technology directly sequences native DNA and RNA molecules. Nanopore sequencing, therefore, avoids converting mRNA into cDNA molecules, which not only allows for the sequencing of extremely long native DNA and full-length RNA molecules but also document modifications that have been made to those native DNA or RNA bases. In this review on direct DNA sequencing and direct RNA sequencing using Oxford Nanopore technology, we focus on their development and application achievements, discussing their challenges and future perspective. We also address the problems researchers may encounter applying these approaches in their research topics, and how to resolve them. Public summary: • Nanopore-seq can dissect native DNA/RNA molecules from any organisms at unlimited length • A wide variety of algorithms greatly increase the accuracy of signal decoding in Nanopore-Seq • Nanopore-Seq significantly facilitates genome assembly and structural variant calling, and can simultaneously detect base modifications • These advantages ensure its great potentials in future medical and agricultural practices

Single Molecule Sequencing (SMS) technologies generate long but noisy reads data. Here, the authors develop Clairvoyante, a deep neural network-based method for variant calling with SMS reads such as PacBio and ONT data.

In this study, we reported the prevalence and mechanism associated with the extended-spectrum beta-lactamase (ESBL)-positive phenotype in Laribacter hongkongensis isolated from patients and fish. Using the inhibition zone enhancement test, 20 (95.2%) of the 21 patient strains and 8 (57.1%) of the 14 fish strains were tested ESBL-positive. However, ESBL genes, including SHV, TEM, CTX-M, GES, and PER, were not detected in all of these 28 L. hongkongensis isolates. No ESBL gene could be detected in either the complete genome of L. hongkongensis HLHK9 or the draft genome of PW3643. PCR and DNA sequencing revealed that all the 35 L. hongkongensis isolates (showing both ESBL-positive and ESBL-negative phenotypes) were positive for the ampC gene. When the AmpC deletion mutant, HLHK9ΔampC, was subject to the zone enhancement test, the difference of zone size between ceftazidime/clavulanate and ceftazidime was less than 5 mm. When boronic acid was added to the antibiotic disks, none of the 28 “ESBL-positive” isolates showed a ≥ 5 mm enhancement of inhibition zone size diameter between ceftazidime/clavulanate and ceftazidime and between cefotaxime/clavulanate and cefotaxime. A high prevalence (80%) of ESBL-positive phenotype is present in L. hongkongensis. Overall, our results suggested that the ESBL-positive phenotype in L. hongkongensis results from the expression of the intrinsic AmpC beta-lactamase. Confirmatory tests should be performed before issuing laboratory reports for L. hongkongensis isolates that are tested ESBL-positive by disk diffusion clavulanate inhibition test.

The pathogenesis of diabetic nephropathy (DN) is accompanied by alterations in biological function and signaling pathways regulated through complex molecular mechanisms. A number of regulatory factors, including transcription factors (TFs) and non-coding RNAs (ncRNAs, including lncRNAs and miRNAs), have been implicated in DN; however, it is unclear how the interactions among these regulatory factors contribute to the development of DN pathogenesis. In this study, we developed a network-based analysis to decipher interplays between TFs and ncRNAs regulating progression of DN by combining omics data with regulatory factor-target information. To accomplish this, we identified differential expression programs of mRNAs and miRNAs during early DN (EDN) and established DN. We then uncovered putative interactive connections among miRNA–mRNA, lncRNA–miRNA, and lncRNA–mRNA implicated in transcriptional control. This led to the identification of two lncRNAs (MALAT1 and NEAT1) and the three TFs (NF-κB, NFE2L2, and PPARG) that likely cooperate with a set of miRNAs to modulate EDN and DN target genes. The results highlight how crosstalk among TFs, lncRNAs, and miRNAs regulate the expression of genes both transcriptionally and post-transcriptionally, and our findings provide new insights into the molecular basis and pathogenesis of progressive DN.

Summary: Single-molecule sequencing technologies produce much longer reads compared with next-generation sequencing, greatly improving the contiguity of de novo assembly of genomes. However, the relatively high error rates in long reads make it challenging to obtain high-quality assemblies. A computationally intensive consensus step is needed to resolve the discrepancies in the reads. Efficient consensus tools have emerged in the recent past, based on partial-order alignment. In this study, we discovered that the spatial relationship of alignment pileup is crucial to high-quality consensus and developed a deep learning-based consensus tool, CONNET, which outperforms the fastest tools in terms of both accuracy and speed. We tested CONNET using a 90× dataset of E. coli and a 37× human dataset. In addition to achieving high-quality consensus results, CONNET is capable of delivering phased diploid genome consensus. Diploid consensus on the above-mentioned human assembly further reduced 12% of the consensus errors made in the haploid results.

Here we describe the sequencing and assembly of the pathogenic fungus Lomentospora prolificans using a combination of short, highly accurate Illumina reads and additional coverage in very long Oxford Nanopore reads. The resulting assembly is highly contiguous, containing a total of 37,627,092 bp with over 98% of the sequence in just 26 scaffolds. Annotation identified 8896 protein-coding genes. Pulsed-field gel analysis suggests that this organism contains at least 7 and possibly 11 chromosomes, the two longest of which have sizes corresponding closely to the sizes of the longest scaffolds, at 6.6 and 5.7 Mb.

Abstract Background The recent release of the gene-targeted metagenomics assembler Xander has demonstrated that using the trained Hidden Markov Model (HMM) to guide the traversal of de Bruijn graph gives obvious advantage over other assembly methods. Xander, as a pilot study, indeed has a lot of room for improvement. Apart from its slow speed, Xander uses only 1 k-mer size for graph construction and whatever choice of k will compromise either sensitivity or accuracy. Xander uses a Bloom-filter representation of de Bruijn graph to achieve a lower memory footprint. Bloom filters bring in false positives, and it is not clear how this would impact the quality of assembly. Xander does not keep track of the multiplicity of k-mers, which would have been an effective way to differentiate between erroneous k-mers and correct k-mers. Results In this paper, we present a new gene-targeted assembler MegaGTA, which attempts to improve Xander in different aspects. Quality-wise, it utilizes iterative de Bruijn graphs to take full advantage of multiple k-mer sizes to make the best of both sensitivity and accuracy. Computation-wise, it employs succinct de Bruijn graphs (SdBG) to achieve low memory footprint and high speed (the latter is benefited from a highly efficient parallel algorithm for constructing SdBG). Unlike Bloom filters, an SdBG is an exact representation of a de Bruijn graph. It enables MegaGTA to avoid false-positive contigs and to easily incorporate the multiplicity of k-mers for building better HMM model. We have compared MegaGTA and Xander on an HMP-defined mock metagenomic dataset, and showed that MegaGTA excelled in both sensitivity and accuracy. On a large rhizosphere soil metagenomic sample (327Gbp), MegaGTA produced 9.7–19.3% more contigs than Xander, and these contigs were assigned to 10–25% more gene references. In our experiments, MegaGTA, depending on the number of k-mers used, is two to ten times faster than Xander. Conclusion MegaGTA improves on the algorithm of Xander and achieves higher sensitivity, accuracy and speed. Moreover, it is capable of assembling gene sequences from ultra-large metagenomic datasets. Its source code is freely available at https://github.com/HKU-BAL/megagta .

Linked-read sequencing, using highly-multiplexed genome partitioning and barcoding, can span hundreds of kilobases to improve de novo assembly, haplotype phasing, and other applications. Based on our analysis of 14 datasets, we introduce LRSim that simulates linked-reads by emulating the library preparation and sequencing process with fine control over variants, linked-read characteristics, and the short-read profile. We conclude from the phasing and assembly of multiple datasets, recommendations on coverage, fragment length, and partitioning when sequencing genomes of different sizes and complexities. These optimizations improve results by orders of magnitude, and enable the development of novel methods. LRSim is available at https://github.com/aquaskyline/LRSIM. Keywords: Linked-read, Molecular barcoding, Reads partitioning, Phasing, Reads simulation, Genome assembly, 10X Genomics

Motivation Mirtrons, a vital category of non-canonical microRNAs (miRNAs) originating from exon-intron boundaries through splicing mechanisms, play crucial roles in cellular processes. However, existing databases lack the latest data and structural information, hindering understandings of mirtron formation and functions. Results We introduce MirtronStructDB, an online database addressing these gaps by incorporating over 350 novel mirtrons. Significantly, it provides corresponding predicted RNA secondary structures, offering a deeper understanding of the functional roles and mechanisms of all mirtrons. This enhances previous repositories, offering a total of 4,209 mirtron records spanning 25 species from 46 publications. Our database contributes for unraveling patterns and functions in mirtrons across species and diverse structural features. MirtronStructDB allows users to freely browse, search, visualize, and download data via a user-friendly interface. Availability MirtronStructDB is accessible at: http://www.bio8.cs.hku.hk/msdb/. Contact lishumin@connect.hku.hk, rbluo@cs.hku.hk

Background: Liver transplantation remains the treatment of choice for a selected group of hepatocellular carcinoma (HCC) patients. However, the long-term benefit is greatly hampered by post-transplant HCC recurrence. Our previous studies have identified liver graft injury as an acute phase event leading to post-transplant tumor recurrence. Methods: To re-examine this acute phase event at the molecular level and in an unbiased way, RNA sequencing (RNA-Seq) was performed on liver graft biopsies obtained from the transplant recipients two hours after portal vein reperfusion with an aim to capture frequently altered pathways that account for post-transplant tumor recurrence. Liver grafts from recurrent recipients (n = 6) were sequenced and compared with those from recipients without recurrence (n = 5). Results: RNA expression profiles comparison pointed to several frequently altered pathways, among which pathways related to cell adhesion molecules were the most involved. Subsequent validation using quantitative polymerase chain reaction confirmed the differential involvement of two cell adhesion molecules HFE (hemochromatosis) and CD274 and their related molecules in the acute phase event. Conclusion: This whole transcriptome strategy unravels the molecular landscape of liver graft gene expression alterations, which can identify key pathways and genes that are involved in acute phase liver graft injury that may lead to post-transplant tumor recurrence.

MOTIVATION:Reproducing the results from a scientific paper can be challenging due to the absence of data and the computational tools required for their analysis. In addition, details relating to the procedures used to obtain the published results can be difficult to discern due to the use of natural language when reporting how experiments have been performed. The Investigation/Study/Assay (ISA), Nanopublications (NP), and Research Objects (RO) models are conceptual data modelling frameworks that can structure such information from scientific papers. Computational workflow platforms can also be used to reproduce analyses of data in a principled manner. We assessed the extent by which ISA, NP, and RO models, together with the Galaxy workflow system, can capture the experimental processes and reproduce the findings of a previously published paper reporting on the development of SOAPdenovo2, a de novo genome assembler. RESULTS:Executable workflows were developed using Galaxy, which reproduced results that were consistent with the published findings. A structured representation of the information in the SOAPdenovo2 paper was produced by combining the use of ISA, NP, and RO models. By structuring the information in the published paper using these data and scientific workflow modelling frameworks, it was possible to explicitly declare elements of experimental design, variables, and findings. The models served as guides in the curation of scientific information and this led to the identification of inconsistencies in the original published paper, thereby allowing its authors to publish corrections in the form of an errata. AVAILABILITY:SOAPdenovo2 scripts, data, and results are available through the GigaScience Database: http://dx.doi.org/10.5524/100044; the workflows are available from GigaGalaxy: http://galaxy.cbiit.cuhk.edu.hk; and the representations using the ISA, NP, and RO models are available through the SOAPdenovo2 case study website http://isa-tools.github.io/soapdenovo2/. CONTACT:philippe.rocca-serra@oerc.ox.ac.uk and susanna-assunta.sansone@oerc.ox.ac.uk.