NURECON: A Novel Online System for Determining Nutrition Requirements Based on Microbial Composition.

Dietary habits have been proven to have an impact on the microbial composition and health of the human gut. Over the past decade, researchers have discovered that gut microbiota can use nutrients to produce metabolites that have major implications for human physiology. However, there is no comprehensive system that specifically focuses on identifying nutrient deficiencies based on gut microbiota, making it difficult to interpret and compare gut microbiome data in the literature. This study proposes an analytical platform, NURECON, that can predict nutrient deficiency information in individuals by comparing their metagenomic information to a reference baseline. NURECON integrates a next-generation bacterial 16S rRNA analytical pipeline (QIIME2), metabolic pathway prediction tools (PICRUSt2 and KEGG), and a food compound database (FooDB) to enable the identification of missing nutrients and provide personalized dietary suggestions. Metagenomic information from total number of 287 healthy subjects was used to establish baseline microbial composition and metabolic profiles. The uploaded data is analyzed and compared to the baseline for nutrient deficiency assessment. Visualization results include gut microbial composition, related enzymes, pathways, and nutrient abundance. NURECON is a user-friendly online platform that provides nutritional advice to support dietitians' research or menu design.

Twnbiome: a public database of the healthy Taiwanese gut microbiome.

With new advances in next generation sequencing (NGS) technology at reduced costs, research on bacterial genomes in the environment has become affordable. Compared to traditional methods, NGS provides high-throughput sequencing reads and the ability to identify many species in the microbiome that were previously unknown. Numerous bioinformatics tools and algorithms have been developed to conduct such analyses. However, in order to obtain biologically meaningful results, the researcher must select the proper tools and combine them to construct an efficient pipeline. This complex procedure may include tens of tools, each of which require correct parameter settings. Furthermore, an NGS data analysis involves multiple series of command-line tools and requires extensive computational resources, which imposes a high barrier for biologists and clinicians to conduct NGS analysis and even interpret their own data. Therefore, we established a public gut microbiome database, which we call Twnbiome, created using healthy subjects from Taiwan, with the goal of enabling microbiota research for the Taiwanese population. Twnbiome provides users with a baseline gut microbiome panel from a healthy Taiwanese cohort, which can be utilized as a reference for conducting case-control studies for a variety of diseases. It is an interactive, informative, and user-friendly database. Twnbiome additionally offers an analysis pipeline, where users can upload their data and download analyzed results. Twnbiome offers an online database which non-bioinformatics users such as clinicians and doctors can not only utilize to access a control set of data, but also analyze raw data with a few easy clicks. All results are customizable with ready-made plots and easily downloadable tables. Database URL: http://twnbiome.cgm.ntu.edu.tw/ .

An Oral Microbial Biomarker for Early Detection of Recurrence of Oral Squamous Cell Carcinoma.

Changes in the oral microbiome are associated with oral squamous cell carcinoma (OSCC). Oral microbe-derived signatures have been utilized as markers of OSCC. However, the structure of the oral microbiome during OSCC recurrence and biomarkers for the prediction of OSCC recurrence remains unknown. To identify OSCC recurrence-associated microbial biomarkers for the prediction of OSCC recurrence, we performed 16S rRNA amplicon sequencing on 54 oral swab samples from OSCC patients. Differences in bacterial compositions were observed in patients with vs without recurrence. We found that Granulicatella, Peptostreptococcus, Campylobacter, Porphyromonas, Oribacterium, Actinomyces, Corynebacterium, Capnocytophaga, and Dialister were enriched in OSCC recurrence. Functional analysis of the oral microbiome showed altered functions associated with OSCC recurrence compared with nonrecurrence. A random forest prediction model was constructed with five microbial signatures including Leptotrichia trevisanii, Capnocytophaga sputigena, Capnocytophaga, Cardiobacterium, and Olsenella to discriminate OSCC recurrence from original OSCC (accuracy = 0.963). Moreover, we validated the prediction model in another independent cohort (46 OSCC patients), achieving an accuracy of 0.761. We compared the accuracy of the prediction of OSCC recurrence between the five microbial signatures and two clinicopathological parameters, including resection margin and lymph node counts. The results predicted by the model with five microbial signatures showed a higher accuracy than those based on the clinical outcomes from the two clinicopathological parameters. This study demonstrated the validity of using recurrence-related microbial biomarkers, a noninvasive and effective method for the prediction of OSCC recurrence. Our findings may contribute to the prognosis and treatment of OSCC recurrence.

Multi-ethnic Imputation System (MI-System): A genotype imputation server for high-dimensional data.

OBJECTIVE: Genotype imputation is a commonly used technique that infers un-typed variants into a study's genotype data, allowing better identification of causal variants in disease studies. However, due to overrepresentation of Caucasian studies, there's a lack of understanding of genetic basis of health-outcomes in other ethnic populations. Therefore, facilitating imputation of missing key-predictor-variants that can potentially improve a risk health-outcome prediction model, specifically for Asian ancestry, is of utmost relevance. METHODS: We aimed to construct an imputation and analysis web-platform, that primarily facilitates, but is not limited to genotype imputation on East-Asians. The goal is to provide a collaborative imputation platform for researchers in the public domain towards rapidly and efficiently conducting accurate genotype imputation. RESULTS: We present an online genotype imputation platform, Multi-ethnic Imputation System (MI-System) (https://misystem.cgm.ntu.edu.tw/), that offers users 3 established pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1 for conducting imputation analyses. In addition to 1000 Genomes and Hapmap3, a new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. MI-System further offers functions to create customized reference panels to be used for imputation, conduct quality control, split whole genome data into chromosomes, and convert genome builds. CONCLUSION: Users can upload their genotype data and perform imputation with minimum effort and resources. The utility functions further can be utilized to preprocess user uploaded data with easy clicks. MI-System potentially contributes to Asian-population genetics research, while eliminating the requirement for high performing computational resources and bioinformatics expertise. It will enable an increased pace of research and provide a knowledge-base for genetic carriers of complex diseases, therefore greatly enhancing patient-driven research. STATEMENT OF SIGNIFICANCE: Multi-ethnic Imputation System (MI-System), primarily facilitates, but is not limited to, imputation on East-Asians, through 3 established prephasing-imputation pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1, where users can upload their genotype data and perform imputation and other utility functions with minimum effort and resources. A new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. Utility functions include (a) create customized reference panels, (b) conduct quality control, (c) split whole genome data into chromosomes, and (d) convert genome builds. Users can also combine 2 reference panels using the system and use combined panels as reference to conduct imputation using MI-System.

Radiation-induced sarcoma of head and neck: Clinical characteristics and molecular signatures.

BACKGROUND: Radiation-induced sarcoma of the head and neck (RISHN) is a rare yet devastating potential complication of radiotherapy treatment. We aimed to evaluate the clinicopathological characteristics and molecular signatures of RISHN in patients who underwent radiotherapy for head and neck cancer (HNC) to identify high-risk patients and enable earlier cancer detection. METHODS: This study retrospectively evaluated 24 sarcoma patients who received radiotherapy for HNC between 1994 and 2019. Patients were divided into two groups based on RISHN latency period. Patient demographics, initial tumor staging, risk factors, and survival between groups were analyzed, and whole-exome sequencing (WES) of selected samples was performed. RESULTS: The median age at diagnosis of RISHN was 54 years, and the male-to-female ratio was 2:1. The latency period ranged from 0.8 to 64.4 years (median 6.5 years), with a median survival of 21.5 months. Primary cancer in the oral cavity, treatment with alkylating agents, alcohol consumption, betel nut chewing, and smoking were identified as risk factors for short (<5 years) latency periods. The majority of RISHN cases occurred in the oral cavity (58.3%). WES analysis showed that tumor necrosis factor and cell cycle checkpoint pathways were differentially involved in both patient groups. CONCLUSIONS: Although case numbers were small, our cohort represents the largest case series of RISHN from a single institution to date. Clinicians must be aware of factors affecting RISHN development and latency, and risk factor identification may lead to earlier detection and prevention in the future.

Spindle Cell Carcinoma of the Head and Neck: Clinical Characteristics and Molecular Signatures.

OBJECTIVE/HYPOTHESIS: Spindle cell carcinoma of the head and neck (HNSpCC) is a rare variant of head and neck squamous cell carcinoma (HNSCC). This study evaluated the clinical characteristics and molecular signatures of such tumors. STUDY DESIGN: Retrospective analysis. METHODS: Medical records of patients diagnosed with HNSpCC from 1996 to 2018 were reviewed. The clinicopathologic features, treatment modalities, and survival status were carefully recorded. Whole exome sequencing (WES) was performed to evaluate the genetic signatures of HNSpCC. RESULTS: We found that among all 71 patients included in this study, the majority of them were male, with tumors developing predominantly in the oral cavity. The 1-, 3-, and 5-year disease-specific survival (DSS) rates were 64.6%, 49.5%, and 43.9%, respectively. A high local recurrence (LR) and distant metastasis (DM) rate (47.9%-25.3%, respectively) were observed. A significant proportion (28.2%) of patients with the worst prognosis had history of previous head and neck cancer (HNC) and had been treated with radiotherapy (RT). WES revealed that those post-RT SpCC shared common mutations with their previous HNC (pre-RT SCC), but gained additional genetic traits, such as hypoxia and cell-ECM interaction that were favorable for survival in an irradiated microenvironment. Distinct genetic landscapes in primary and post-RT SpCC were also found. CONCLUSIONS: This study demonstrates that HNSpCC is a unique entity with more aggressive behavior than conventional HNSCC. HNSpCC arising from a previously irradiated field is a predictor of dismal survival. Both genetic and microenvironmental factors contribute to this highly invasive tumor. LEVEL OF EVIDENCE: 4 Laryngoscope, 133:2183-2191, 2023.

Generation of induced pluripotent stem cells from Bornean orangutans.

Introduction: Orangutans, classified under the Pongo genus, are an endangered non-human primate (NHP) species. Derivation of induced pluripotent stem cells (iPSCs) represents a promising avenue for conserving the genetic resources of these animals. Earlier studies focused on deriving orangutan iPSCs (o-iPSCs) from Sumatran orangutans (Pongo abelii). To date, no reports specifically target the other Critically Endangered species in the Pongo genus, the Bornean orangutans (Pongo pygmaeus). Methods: Using Sendai virus-mediated Yamanaka factor-based reprogramming of peripheral blood mononuclear cells to generate iPSCs (bo-iPSCs) from a female captive Bornean orangutan. In this study, we evaluate the colony morphology, pluripotent markers, X chromosome activation status, and transcriptomic profile of the bo-iPSCs to demonstrate the pluripotency of iPSCs from Bornean orangutans. Results: The bo-iPSCs were successfully derived from Bornean orangutans, using Sendai virus-mediated Yamanaka factor-based reprogramming of peripheral blood mononuclear cells. When a modified 4i/L/A (m4i/L/A) culture system was applied to activate the WNT signaling pathway in these bo-iPSCs, the derived cells (m-bo-iPSCs) manifested characteristics akin to human naive pluripotent stem cells, including high expression levels of KLF17, DNMT3L, and DPPA3/5, as well as the X chromosome reactivation. Comparative RNA-seq analysis positioned the m-bo-iPSCs between human naive and formative pluripotent states. Furthermore, the m-bo-iPSCs express differentiation capacity into all three germlines, evidenced by controlled in vitro embryoid body formation assay. Discussion: Our work establishes a novel approach to preserve the genetic diversity of endangered Bornean orangutans while offering insights into primate stem cell pluripotency. In the future, derivation of the primordial germ cell-like cells (PGCLCs) from m-bo-iPSCs is needed to demonstrate the further specific application in species preservation and broaden the knowledge of primordial germ cell specification across species.

16S-ITGDB: An Integrated Database for Improving Species Classification of Prokaryotic 16S Ribosomal RNA Sequences.

Analyzing 16S ribosomal RNA (rRNA) sequences allows researchers to elucidate the prokaryotic composition of an environment. In recent years, third-generation sequencing technology has provided opportunities for researchers to perform full-length sequence analysis of bacterial 16S rRNA. RDP, SILVA, and Greengenes are the most widely used 16S rRNA databases. Many 16S rRNA classifiers have used these databases as a reference for taxonomic assignment tasks. However, some of the prokaryotic taxonomies only exist in one of the three databases. Furthermore, Greengenes and SILVA include a considerable number of taxonomies that do not have the resolution to the species level, which has limited the classifiers' performance. In order to improve the accuracy of taxonomic assignment at the species level for full-length 16S rRNA sequences, we manually curated the three databases and removed the sequences that did not have a species name. We then established a taxonomy-based integrated database by considering both taxonomies and sequences from all three 16S rRNA databases and validated it by a mock community. Results showed that our taxonomy-based integrated database had improved taxonomic resolution to the species level. The integrated database and the related datasets are available at https://github.com/yphsieh/ItgDB.

Regulatory mechanisms and function of hypoxia-induced long noncoding RNA NDRG1-OT1 in breast cancer cells.

Hypoxia is a classic feature of the tumor microenvironment that has profound effects on cancer progression and is tightly associated with poor prognosis. Long noncoding RNAs (lncRNAs), a component of the noncoding genome, have been increasingly investigated due to their diverse roles in tumorigenesis. Previously, a hypoxia-induced lncRNA, NDRG1-OT1, was identified in MCF-7 breast cancer cells using next-generation sequencing. However, the regulatory mechanisms of NDRG1-OT1 remain elusive. Therefore, the purpose of this study was to investigate the regulatory mechanisms and functional roles of NDRG1-OT1 in breast cancer cells. Expression profiling of NDRG1-OT1 revealed that it was upregulated under hypoxia in different breast cancer cells. Overexpression and knockdown of HIF-1α up- and downregulated NDRG1-OT1, respectively. Luciferase reporter assays and chromatin immunoprecipitation assays validated that HIF-1α transcriptionally activated NDRG1-OT1 by binding to its promoter (-1773 to -1769 and -647 to -643 bp). Next, to investigate whether NDRG1-OT1 could function as a miRNA sponge, results of in silico analysis, expression profiling of predicted miRNAs, and RNA immunoprecipitation assays indicated that NDRG1-OT1 could act as a miRNA sponge of miR-875-3p. In vitro and in vivo functional assays showed that NDRG1-OT1 could promote tumor growth and migration. Lastly, a small peptide (66 a.a.) translated from NDRG1-OT1 was identified. In summary, our findings revealed novel regulatory mechanisms of NDRG1-OT1 by HIF-1α and upon miR-875-3p. Also, NDRG1-OT1 promoted the malignancy of breast cancer cells and encoded a small peptide.

Comparison of DNA Methylation Changes Between the Gestation Period and the After-Delivery State: A Pilot Study of 10 Women.

Background: Gestational adaptation occurs soon after fertilization and continues throughout pregnancy, whereas women return to a pre-pregnancy state after delivery and lactation. However, little is known about the role of DNA methylation in fine-tuning maternal physiology. Understanding the changes in DNA methylation during pregnancy is the first step in clarifying the association of diet, nutrition, and thromboembolism with the changes in DNA methylation. In this study, we investigated whether and how the DNA methylation pattern changes in the three trimesters and after delivery in ten uncomplicated pregnancies. Results: DNA methylation was measured using a Human MethylationEPIC BeadChip. There were 14,018 cytosine-guanine dinucleotide (CpG) sites with statistically significant changes in DNA methylation over the four time periods (p < 0.001). Overall, DNA methylation after delivery was higher than that of the three trimesters (p < 0.001), with the protein ubiquitination pathway being the top canonical pathway involved. We classified the CpG sites into nine groups according to the changes in the three trimesters and found that 38.37% of CpG sites had DNA methylation changes during pregnancy, especially between the first and second trimesters. Conclusion: DNA methylation pattern changes between trimesters, indicating possible involvement in maternal adaptation to pregnancy. Meanwhile, DNA methylation patterns during pregnancy and in the postpartum period were different, implying that puerperium repair may also function through DNA methylation mechanisms.

Uremic Toxin-Producing Bacteroides Species Prevail in the Gut Microbiota of Taiwanese CKD Patients: An Analysis Using the New Taiwan Microbiome Baseline.

Rationale and Objective: Gut microbiota have been targeted by alternative therapies for non-communicable diseases. We examined the gut microbiota of a healthy Taiwanese population, identified various bacterial drivers in different demographics, and compared them with dialysis patients to associate kidney disease progression with changes in gut microbiota. Study Design: This was a cross-sectional cohort study. Settings and Participants: Fecal samples were obtained from 119 healthy Taiwanese volunteers, and 16S rRNA sequencing was done on the V3-V4 regions to identify the bacterial enterotypes. Twenty-six samples from the above cohort were compared with fecal samples from 22 peritoneal dialysis and 16 hemodialysis patients to identify species-level bacterial biomarkers in the dysbiotic gut of chronic kidney disease (CKD) patients. Results: Specific bacterial species were identified pertaining to different demographics such as gender, age, BMI, physical activity, and sleeping habits. Dialysis patients had a significant difference in gut microbiome composition compared to healthy controls. The most abundant genus identified in CKD patients was Bacteroides, and at the species level hemodialysis patients showed significant abundance in B. ovatus, B. caccae, B. uniformis, and peritoneal dialysis patients showed higher abundance in Blautia producta (p ≤ 0.05) than the control group. Pathways pertaining to the production of uremic toxins were enriched in CKD patients. The abundance of the bacterial species depended on the type of dialysis treatment. Conclusion: This study characterizes the healthy gut microbiome of a Taiwanese population in terms of various demographics. In a case-control examination, the results showed the alteration in gut microbiota in CKD patients corresponding to different dialysis treatments. Also, this study identified the bacterial species abundant in CKD patients and their possible role in complicating the patients' condition.

The Gut Microbiome, Seleno-Compounds, and Acute Myocardial Infarction.

Background: Gut microbiome alterations might be considered a metabolic disorder. However, the relationship between the microbiome and acute myocardial infarction (AMI) has not been properly validated. Methods: The feces of 44 subjects (AMI: 19; control: 25) were collected for fecal genomic DNA extraction. The variable region V3−V4 of the 16S rRNA gene was sequenced using the Illumina MiSeq platform. The metabolite amounts were analyzed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways. Results: The bacteria were more enriched in the AMI group both in the observed operational taxonomic units (OTUs) and faith phylogenetic diversity (PD) (p-value = 0.01 and <0.001 with 95% CI, individually). The Selenomonadales were less enriched in the AMI group at the family, genus, and species levels (all linear discriminant analysis (LDA) scores > 2). Seleno-compounds were more abundant in the AMI group at the family, genus, and species levels (all LDA scores > 2). Conclusions: This is the first study to demonstrate the association of Selenomonadales and seleno-compounds with the occurrence of AMI. Our findings provide an opportunity to identify a novel approach to prevent and treat AMI.

To compare the performance of prokaryotic taxonomy classifiers using curated 16S full-length rRNA sequences.

BACKGROUND: Taxonomic assignment is a vital step in the analytic pipeline of bacterial 16S ribosomal RNA (rRNA) sequencing. Over the past decade, most research in this field used next-generation sequencing technology to target V3∼V4 regions to analyze bacterial composition. However, focusing on only one or two hypervariable regions limited the taxonomic resolution to the species level. In recent years, third-generation sequencing technology has allowed researchers to easily access full-length prokaryotic 16S sequences and presented an opportunity to attain greater taxonomic depth. However, the accuracy of current taxonomic classifiers in analyzing 16S full-length sequence analysis remains unclear. OBJECTIVE: The purpose of this study is to compare the accuracy of several widely-used 16S sequence classifiers and to indicate the most suitable 16S training dataset for each classifier. METHODS: Both curated 16S full-length sequences and cross-validation datasets were used to validate the performance of seven classifiers, including QIIME2, mothur, SINTAX, SPINGO, Ribosomal Database Project (RDP), IDTAXA, and Kraken2. Different sequence training datasets, such as SILVA, Greengenes, and RDP, were used to train the classification models. RESULTS: The accuracy of each classifier to the species levels were illustrated. According to the experimental results, using RDP sequences as the training data, SINTAX and SPINGO provided the highest accuracy, and were recommended for the task of classifying prokaryotic 16S full-length rRNA sequences. CONCLUSION: The performance of the classifiers was affected by sequence training datasets. Therefore, different classifiers should use the most suitable 16S training data to improve the accuracy and taxonomy resolution in the taxonomic assignment.

Extracellular domain of semaphorin 5A serves a tumor­suppressing role by activating interferon signaling pathways in lung adenocarcinoma cells.

Semaphorin 5A (SEMA5A), which was originally identified as an axon guidance molecule in the nervous system, has been subsequently identified as a prognostic biomarker for lung cancer in nonsmoking women. SEMA5A acts as a tumor suppressor by inhibiting the proliferation and migration of lung cancer cells. However, the regulatory mechanism of SEMA5A is not clear. Therefore, the purpose of the present study was to explore the roles of different domains of SEMA5A in its tumor­suppressive effects in lung adenocarcinoma cell lines. First, it was revealed that overexpression of full length SEMA5A or its extracellular domain significantly inhibited the proliferation and migration of both A549 and H1299 cells using MTT, colony formation and gap closure assays. Next, microarray analyses were performed to identify genes regulated by different domains of SEMA5A. Among the differentially expressed genes, the most significant function of these genes that were enriched was the 'Interferon Signaling' pathway according to Ingenuity Pathway Analysis. The activation of the 'Interferon Signaling' pathway was validated by reverse transcription­quantitative PCR and western blotting. In summary, the present study demonstrated that the extracellular domain of SEMA5A could upregulate genes in interferon signaling pathways, resulting in suppressive effects in lung adenocarcinoma cells.

Novel Tumor-Specific Antigens for Immunotherapy Identified From Multi-omics Profiling in Thymic Carcinomas.

Thymic carcinoma (TC) is the most aggressive thymic epithelial neoplasm. TC patients with microsatellite instability, whole-genome doubling, or alternative tumor-specific antigens from gene fusion are most likely to benefit from immunotherapies. However, due to the rarity of this disease, how to prioritize the putative biomarkers and what constitutes an optimal treatment regimen remains largely unknown. Therefore, we integrated genomic and transcriptomic analyses from TC patients and revealed that frameshift indels in KMT2C and CYLD frequently produce neoantigens. Moreover, a median of 3 fusion-derived neoantigens was predicted across affected patients, especially the CATSPERB-TC2N neoantigens that were recurrently predicted in TC patients. Lastly, potentially actionable alterations with early levels of evidence were uncovered and could be used for designing clinical trials. In summary, this study shed light on our understanding of tumorigenesis and presented new avenues for molecular characterization and immunotherapy in TC.

MutScape: an analytical toolkit for probing the mutational landscape in cancer genomics.

Cancer genomics has been evolving rapidly, fueled by the emergence of numerous studies and public databases through next-generation sequencing technologies. However, the downstream programs used to preprocess and analyze data on somatic mutations are scattered in different tools, most of which require specific input formats. Here, we developed a user-friendly Python toolkit, MutScape, which provides a comprehensive pipeline of filtering, combination, transformation, analysis and visualization for researchers, to easily explore the cohort-based mutational characterization for studying cancer genomics when obtaining somatic mutation data. MutScape not only can preprocess millions of mutation records in a few minutes, but also offers various analyses simultaneously, including driver gene detection, mutational signature, large-scale alteration identification and actionable biomarker annotation. Furthermore, MutScape supports somatic variant data in both variant call format and mutation annotation format, and leverages caller combination strategies to quickly eliminate false positives. With only two simple commands, robust results and publication-quality images are generated automatically. Herein, we demonstrate the ability of MutScape to correctly reproduce known results using breast cancer samples from The Cancer Genome Atlas. More significantly, discovery of novel results in cancer genomic studies is enabled through the advanced features in MutScape. MutScape is freely available on GitHub, at https://github.com/anitalu724/MutScape.

Transcriptome profiling reveals the developmental regulation of NaCl-treated Forcipomyia taiwana eggs.

BACKGROUND: The biting midge, Forcipomyia taiwana, is one of the most annoying blood-sucking pests in Taiwan. Current chemical control methods only target the adult, not the immature stages (egg to pupa), of F. taiwana. Discovering new or alternative tactics to enhance or replace existing methods are urgently needed to improve the effectiveness of F. taiwana control. The egg is the least understood life stage in this pest species but may offer a novel point of control as addition of NaCl to the egg environment inhibits development. Thus, the objective of this study was to use RNA profiling to better understand the developmental differences between wild-type melanized (black) and NaCl-induced un-melanized (pink), infertile F. taiwana eggs. RESULTS: After de novo assembly with Trinity, 87,415 non-redundant transcripts (Ft-nr) with an N50 of 1099 were obtained. Of these, 26,247 (30%) transcripts were predicted to have long open reading frames (ORFs, defined here as ≥300 nt) and 15,270 (17.5%) transcripts have at least one predicted functional domain. A comparison between two biological replicates each of black and pink egg samples, although limited in sample size, revealed 5898 differentially expressed genes (DEGs; 40.9% of the transcripts with long ORFs) with ≥2-fold difference. Of these, 2030 were annotated to a Gene Ontology biological process and along with gene expression patterns can be separated into 5 clusters. KEGG pathway analysis revealed that 1589 transcripts could be assigned to 18 significantly enriched pathways in 2 main categories (metabolism and environmental information processing). As expected, most (88.32%) of these DEGs were down-regulated in the pink eggs. Surprisingly, the majority of genes associated with the pigmentation GO term were up-regulated in the pink egg samples. However, the two key terminal genes of the melanin synthesis pathway, laccase2 and DCE/yellow, were significantly down-regulated, and further verified by qRT-PCR. CONCLUSION: We have assembled and annotated the first egg transcriptome for F. taiwana, a biting midge. Our results suggest that down-regulation of the laccase2 and DCE/yellow genes might be the mechanism responsible for the NaCl-induced inhibition of melanization of F. taiwana eggs.

Differential whole-genome doubling and homologous recombination deficiencies across breast cancer subtypes from the Taiwanese population.

Whole-genome doubling (WGD) is an early macro-evolutionary event in tumorigenesis, involving the doubling of an entire chromosome complement. However, its impact on breast cancer subtypes remains unclear. Here, we performed a comprehensive and quantitative analysis of WGD and its influence on breast cancer subtypes in patients from Taiwan and consequently highlight the genomic association between WGD and homologous recombination deficiency (HRD). A higher manifestation of WGD was reported in triple-negative breast cancer, conferring high chromosomal instability (CIN), while HER2 + tumors exhibited early WGD events, with widely varied CIN levels, compared to luminal-type tumors. An association of higher activity of de novo indel signature 2 with WGD and HRD in Taiwanese breast cancer patients was reported. A control test between WGD and pseudo non-WGD samples was further employed to support this finding. The study provides a better comprehension of tumorigenesis in breast cancer subtypes, thus assisting in personalized treatment.

High-performance deep learning pipeline predicts individuals in mixtures of DNA using sequencing data.

In this study, we proposed a deep learning (DL) model for classifying individuals from mixtures of DNA samples using 27 short tandem repeats and 94 single nucleotide polymorphisms obtained through massively parallel sequencing protocol. The model was trained/tested/validated with sequenced data from 6 individuals and then evaluated using mixtures from forensic DNA samples. The model successfully identified both the major and the minor contributors with 100% accuracy for 90 DNA mixtures, that were manually prepared by mixing sequence reads of 3 individuals at different ratios. Furthermore, the model identified 100% of the major contributors and 50-80% of the minor contributors in 20 two-sample external-mixed-samples at ratios of 1:39 and 1:9, respectively. To further demonstrate the versatility and applicability of the pipeline, we tested it on whole exome sequence data to classify subtypes of 20 breast cancer patients and achieved an area under curve of 0.85. Overall, we present, for the first time, a complete pipeline, including sequencing data processing steps and DL steps, that is applicable across different NGS platforms. We also introduced a sliding window approach, to overcome the sequence length variation problem of sequencing data, and demonstrate that it improves the model performance dramatically.

MiDSystem: A comprehensive online system for de novo assembly and analysis of microbial genomes.

The substantial reduction in experimental cost of next-generation sequencing techniques makes it feasible to assemble a bacterial genome of unknown species de novo and acquire substantial genetic information from environmental samples. Many bioinformatics tools and algorithms have also been developed for prokaryotes, but complex parameter settings and command line-based user interfaces cause a significant entry barrier for novices. Efficient construction of pipelines that integrate all the available genomic data poses a major challenge to the understanding of unknown pathogens. MiDSystem is a comprehensive online system for analyzing genomic data from microbiomes. With a user-friendly interface, MiDSystem supports both de novo assembly and metagenomic analysis pipelines. It is designed to automatically analyze whole genome shotgun sequencing data of bacteria submitted by users. Multiple analytical steps can be performed directly on the system, and the results generated from the embedded tools are visualized in an online summary report to make it more interpretable. Constructing a genome de novo has gradually become the foundation of bacterial studies. Taking both single species and metagenomic samples into consideration, MiDSystem can greatly reduce the time and effort for analysis of bacterial genomic data. Use of MiDSystem will enable more focus to be placed on understanding the etiology of bacterial infections and microorganism ecologies.