Decoding the genome of bloodsucking midge Forcipomyia taiwana (Diptera: Ceratopogonidae): Insights into odorant receptor expansion.

Biting midges, notably those within the Ceratopogonidae family, have long been recognized for their epidemiological significance, both as nuisances and vectors for disease transmission in vertebrates. Despite their impact, genomic insights into these insects, particularly beyond the Culicoides genus, remain limited. In this study, we assembled the Forcipomyia taiwana (Shiraki) genome, comprising 113 scaffolds covering 130.4 Mbps-with the longest scaffold reaching 7.6 Mbps and an N50 value of 2.6 Mbps-marking a pivotal advancement in understanding the genetic architecture of ceratopogonid biting midges. Phylogenomic analyses reveal a shared ancestry between F. taiwana and Culicoides sonorensis Wirth & Jones, dating back approximately 124 million years, and highlight a dynamic history of gene family expansions and contractions within the Ceratopogonidae family. Notably, a substantial expansion of the odorant receptor (OR) gene family was observed, which is crucial for the chemosensory capabilities that govern biting midges' interactions with their environment, including host seeking and oviposition behaviors. The distribution of OR genes across the F. taiwana genome displays notable clusters on scaffolds, indicating localized tandem gene duplication events. Additionally, several collinear regions were identified, hinting at segmental duplications, inversions, and translocations, contributing to the olfactory system's evolutionary complexity. Among the 156 ORs identified in F. taiwana, 134 are biting midge-specific ORs, distributed across three distinct clades, each exhibiting unique motif features that distinguish them from the others. Through weighted gene co-expression network analysis, we correlated distinct gene modules with sex and reproductive status, laying the groundwork for future investigations into the interplay between gene expression and adaptive behaviors in F. taiwana. In conclusion, our study not only highlights the unique olfactory repertoire of ceratopogonid biting midges but also sets the stage for future studies into the genetic underpinnings of their unique biological traits and ecological strategies.

Identification of Sexually Dimorphic Genes in Pectoral Fin as Molecular Markers for Assessing the Sex of Japanese Silver Eels (Anguilla japonica).

The Japanese eel (Anguilla japonica) is an important species in East Asian aquaculture. However, the production of seedlings for this purpose still depends on natural resources, as the commercial production of glass eels is not yet possible. Confusion about the sex of silver eels is one of the factors affecting the success rate of artificial maturation. This study sought to devise a harmless method to precisely assess the sex of silver eels. Partial pectoral fins were collected from females and males and the total RNA was extracted for transcriptomic analysis to identify sexually dimorphic genes as molecular markers for sex typing. An online database was constructed to integrate the annotations of transcripts and perform comparative transcriptome analysis. This analysis identified a total of 29 candidate sexually dimorphic genes. Ten were selected for a real-time quantitative polymerase chain reaction (RT-qPCR) to validate the transcriptomic data and evaluate their feasibility as markers. The transcriptomic analysis and RT-qPCR data implicated three potential markers (LOC111853410, kera, and dcn) in sex typing. The expression of LOC111853410 was higher in females than in males. In contrast, the expression of kera and dcn was higher in males than in females. The ΔCT values of three markers were analyzed to determine their inferred thresholds, which can be used to determine the sex of Japanese eels. The results suggested that if a silver eel had a pectoral fin with the pectoral fin having the ΔCT of LOC111853410 < 11.3, the ΔCT of kera > 11.4, or the ΔCT of dcn > 6.5 can be assessed it could be assessed as female. Males could be assessed by the ΔCT of LOC111853410 > 11.3, the ΔCT of kera < 11.4, or the ΔCT of dcn < 6.5 in their pectoral fins. The molecular functions of these markers and the biological significance of their differential expression require further exploration.

Intelligent De Novo Design of Novel Antimicrobial Peptides against Antibiotic-Resistant Bacteria Strains.

Because of the growing number of clinical antibiotic resistance cases in recent years, novel antimicrobial peptides (AMPs) may be ideal for next-generation antibiotics. This study trained a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based on known AMPs to generate novel AMP candidates. The quality of the GAN-designed peptides was evaluated in silico, and eight of them, named GAN-pep 1-8, were selected by an AMP Artificial Intelligence (AI) classifier and synthesized for further experiments. Disc diffusion testing and minimum inhibitory concentration (MIC) determinations were used to identify the antibacterial effects of the synthesized GAN-designed peptides. Seven of the eight synthesized GAN-designed peptides displayed antibacterial activity. Additionally, GAN-pep 3 and GAN-pep 8 presented a broad spectrum of antibacterial effects and were effective against antibiotic-resistant bacteria strains, such as methicillin-resistant Staphylococcus aureus and carbapenem-resistant Pseudomonas aeruginosa. GAN-pep 3, the most promising GAN-designed peptide candidate, had low MICs against all the tested bacteria. In brief, our approach shows an efficient way to discover AMPs effective against general and antibiotic-resistant bacteria strains. In addition, such a strategy also allows other novel functional peptides to be quickly designed, identified, and synthesized for validation on the wet bench.

Zebrafish models for glucocorticoid-induced osteoporosis.

Glucocorticoid-induced osteoporosis (GIOP) is the most common form of secondary osteoporosis due to excessive or long-term glucocorticoid administration, disturbing the homeostasis between bone formation and bone resorption. The bone biology of zebrafish shares a high degree of similarities with mammals. In terms of molecular level, genes and signaling pathways related to skeletogenesis are also highly correlated between zebrafish and humans. Therefore, zebrafish have been utilized to develop multiple GIOP models. Taking advantage of the transparency of zebrafish larvae, their skeletal development and bone mineralization can be readily visualized through in vivo staining without invasive experimental handlings. Moreover, the feasibility of using scales or fin rays to study bone remodeling makes adult zebrafish an ideal model for GIOP research. Here, we reviewed current zebrafish models for GIOP research, focused on the tools and methods established for examining bone homeostasis. As an in vivo, convenient, and robust model, zebrafish have an advantage in performing high-throughput drug screening and could be used to investigate the action mechanisms of therapeutic drugs.

Gut microbiota composition in chemotherapy and targeted therapy of patients with metastatic colorectal cancer.

Studies have reported the effects of the gut microbiota on colorectal cancer (CRC) chemotherapy, but few studies have investigated the association between gut microbiota and targeted therapy. This study investigated the role of the gut microbiota in the treatment outcomes of patients with metastatic CRC (mCRC). We enrolled 110 patients with mCRC and treated them with standard cancer therapy. Stool samples were collected before administering a combination of chemotherapy and targeted therapy. Patients who had a progressive disease (PD) or partial response (PR) for at least 12 cycles of therapy were included in the study. We further divided these patients into anti-epidermal growth factor receptor (cetuximab) and anti-vascular endothelial growth factor (bevacizumab) subgroups. The gut microbiota of the PR group and bevacizumab-PR subgroup exhibited significantly higher α-diversity. The ß-diversity of bacterial species significantly differed between the bevacizumab-PR and bevacizumab-PD groups (P = 0.029). Klebsiella quasipneumoniae exhibited the greatest fold change in abundance in the PD group than in the PR group. Lactobacillus and Bifidobacterium species exhibited higher abundance in the PD group. The abundance of Fusobacterium nucleatum was approximately 32 times higher in the PD group than in the PR group. A higher gut microbiota diversity was associated with more favorable treatment outcomes in the patients with mCRC. Bacterial species analysis of stool samples yielded heterogenous results. K. quasipneumoniae exhibited the greatest fold change in abundance among all bacterial species in the PD group. This result warrants further investigation especially in a Taiwanese population.

Enrichment of Prevotella intermedia in human colorectal cancer and its additive effects with Fusobacterium nucleatum on the malignant transformation of colorectal adenomas.

BACKGROUND: Owing to the heterogeneity of microbiota among individuals and populations, only Fusobacterium nucleatum and Bacteroides fragilis have been reported to be enriched in colorectal cancer (CRC) in multiple studies. Thus, the discovery of additional bacteria contributing to CRC development in various populations can be expected. We aimed to identify bacteria associated with the progression of colorectal adenoma to carcinoma and determine the contribution of these bacteria to malignant transformation in patients of Han Chinese origin. METHODS: Microbiota composition was determined through 16S rRNA V3-V4 amplicon sequencing of autologous adenocarcinomas, adenomatous polyps, and non-neoplastic colon tissue samples (referred to as "tri-part samples") in patients with CRC. Enriched taxa in adenocarcinoma tissues were identified through pairwise comparison. The abundance of candidate bacteria was quantified through genomic quantitative polymerase chain reaction (qPCR) in tissue samples from 116 patients. Associations of candidate bacteria with clinicopathological features and genomic and genetic alterations were evaluated through odds ratio tests. Additionally, the effects of candidate bacteria on CRC cell proliferation, migration, and invasion were evaluated through the co-culture of CRC cells with bacterial cells or with conditioned media from bacteria. RESULTS: Prevotella intermedia was overrepresented in adenocarcinomas compared with paired adenomatous polyps. Furthermore, co-abundance of P. intermedia and F. nucleatum was observed in tumor tissues. More notably, the coexistence of these two bacteria in adenocarcinomas was associated with lymph node involvement and distant metastasis. These two bacteria also exerted additive effects on the enhancement of the migration and invasion abilities of CRC cells. Finally, conditioned media from P. intermedia promoted the migration and invasion of CRC cells. CONCLUSION: This report is the first to demonstrate that P. intermedia is enriched in colorectal adenocarcinoma tissues and enhances the migration and invasion abilities of CRC cells. Moreover, P. intermedia and F. nucleatum exert additive effects on the malignant transformation of colorectal adenomas into carcinomas. These findings can be used to identify patients at a high risk of malignant transformation of colorectal adenomas or metastasis of CRC, and they can accordingly be provided optimal clinical management.

The gut microbiota regulates acute foreign body reaction and tissue repair after biomaterial implantation.

We hypothesized that the host microbiome may influence foreign body responses following biomaterial implantation. To test this, we implanted a variety of clinically relevant biomaterials into germ-free or antibiotic-treated mice. Surprisingly, these mice displayed less fibrous tissue deposition, reduced host cell recruitment to the implant site, and differential expression of angiogenic and inflammatory markers. These observations were reversed upon fecal microbiome reconstitution, confirming a causal role of the host microbiome. In a clinically relevant disease model, microbiome-depleted mice cleared hyaluronic acid and bone marrow mononuclear cells from ischemic hind limb tissues more slowly, resulting in an improved therapeutic response. Findings were confirmed in pigs which showed reduced fibrotic responses to a variety of implanted materials. Lastly, we profiled changes in the host microbiome following material implantation, implicating several key bacteria phyla.

Differences in gut microbiota correlate with symptoms and regional brain volumes in patients with late-life depression.

Depression is associated with gut dysbiosis that disrupts a gut-brain bidirectional axis. Gray matter volume changes in cortical and subcortical structures, including prefrontal regions and the hippocampus, have also been noted in depressive disorders. However, the link between gut microbiota and brain structures in depressed patients remains elusive. Neuropsychiatric measures, stool samples, and structural brain images were collected from 36 patients with late-life depression (LLD) and 17 healthy controls. 16S ribosomal RNA (rRNA) gene sequencing was used to profile stool microbial communities for quantitation of microbial composition, abundance, and diversity. T1-weighted brain images were assessed with voxel-based morphometry to detect alterations in gray matter volume between groups. Correlation analysis was performed to identify the possible association between depressive symptoms, brain structures and gut microbiota. We found a significant difference in the gut microbial composition between patients with late-life depression (LLD) and healthy controls. The genera Enterobacter and Burkholderia were positively correlated with depressive symptoms and negatively correlated with brain structural signatures in regions associated with memory, somatosensory integration, and emotional processing/cognition/regulation. Our study purports the microbiota-gut-brain axis as a potential mechanism mediating the symptomatology of LLD patients, which may facilitate the development of therapeutic strategies targeting gut microbes in the treatment of elderly depressed patients.

Elegancy: Digitizing the wisdom from laboratories to the cloud with free no-code platform.

One of the top priorities in any laboratory is archiving experimental data in the most secure, efficient, and errorless way. It is especially important to those in chemical and biological research, for it is more likely to damage experiment records. In addition, the transmission of experiment results from paper to electronic devices is time-consuming and redundant. Therefore, we introduce an open-source no-code electronic laboratory notebook, Elegancy, a cloud-based/standalone web service distributed as a Docker image. Elegancy fits all laboratories but is specially equipped with several features benefitting biochemical laboratories. It can be accessed via various web browsers, allowing researchers to upload photos or audio recordings directly from their mobile devices. Elegancy also contains a meeting arrangement module, audit/revision control, and laboratory supply management system. We believe Elegancy could help the scientific research community gather evidence, share information, reorganize knowledge, and digitize laboratory works with greater ease and security.

A metagenomics study of hexabromocyclododecane degradation with a soil microbial community.

Hexabromocyclododecanes (HBCDs) are globally prevalent and persistent organic pollutants (POPs) listed by the Stockholm Convention in 2013. They have been detected in many environmental media from waterbodies to Plantae and even in the human body. Due to their highly bioaccumulative characterization, they pose an urgent public health issue. Here, we demonstrate that the indigenous microbial community in the agricultural soil in Taiwan could decompose HBCDs with no additional carbon source incentive. The degradation kinetics reached 0.173 day-1 after the first treatment and 0.104 day-1 after second exposure. With additional C-sources, the rate constants decreased to 0.054-0.097 day-1. The hydroxylic debromination metabolites and ring cleavage long-chain alkane metabolites were identified to support the potential metabolic pathways utilized by the soil microbial communities. The metagenome established by Nanopore sequencing showed significant compositional alteration in the soil microbial community after the HBCD treatment. After ranking, comparing relative abundances, and performing network analyses, several novel bacterial taxa were identified to contribute to HBCD biotransformation, including Herbaspirillum, Sphingomonas, Brevundimonas, Azospirillum, Caulobacter, and Microvirga, through halogenated / aromatic compound degradation, glutathione-S-transferase, and hydrolase activity. We present a compelling and applicable approach combining metagenomics research, degradation kinetics, and metabolomics strategies, which allowed us to decipher the natural attenuation and remediation mechanisms of HBCDs.

MetaSquare: an integrated metadatabase of 16S rRNA gene amplicon for microbiome taxonomic classification.

MOTIVATION: Taxonomic classification of 16S ribosomal RNA gene amplicon is an efficient and economic approach in microbiome analysis. 16S rRNA sequence databases like SILVA, RDP, EzBioCloud and HOMD used in downstream bioinformatic pipelines have limitations on either the sequence redundancy or the delay on new sequence recruitment. To improve the 16S rRNA gene-based taxonomic classification, we merged these widely used databases and a collection of novel sequences systemically into an integrated resource. RESULTS: MetaSquare version 1.0 is an integrated 16S rRNA sequence database. It is composed of more than 6 million sequences and improves taxonomic classification resolution on both long-read and short-read methods. AVAILABILITY AND IMPLEMENTATION: Accessible at https://hub.docker.com/r/lsbnb/metasquare_db and https://github.com/lsbnb/MetaSquare. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Galectin-1 orchestrates an inflammatory tumor-stroma crosstalk in hepatoma by enhancing TNFR1 protein stability and signaling in carcinoma-associated fibroblasts.

Most cases of hepatocellular carcinoma (HCC) arise with the fibrotic microenvironment where hepatic stellate cells (HSCs) and carcinoma-associated fibroblasts (CAFs) are critical components in HCC progression. Therefore, CAF normalization could be a feasible therapy for HCC. Galectin-1 (Gal-1), a ß-galactoside-binding lectin, is critical for HSC activation and liver fibrosis. However, few studies has evaluated the pathological role of Gal-1 in HCC stroma and its role in hepatic CAF is unclear. Here we showed that Gal-1 mainly expressed in HCC stroma, but not cancer cells. High expression of Gal-1 is correlated with CAF markers and poor prognoses of HCC patients. In co-culture systems, targeting Gal-1 in CAFs or HSCs, using small hairpin (sh)RNAs or an therapeutic inhibitor (LLS30), downregulated plasminogen activator inhibitor-2 (PAI-2) production which suppressed cancer stem-like cell properties and invasion ability of HCC in a paracrine manner. The Gal-1-targeting effect was mediated by increased a disintegrin and metalloprotease 17 (ADAM17)-dependent TNF-receptor 1 (TNFR1) shedding/cleavage which inhibited the TNF-α → JNK → c-Jun/ATF2 signaling axis of pro-inflammatory gene transcription. Silencing Gal-1 in CAFs inhibited CAF-augmented HCC progression and reprogrammed the CAF-mediated inflammatory responses in a co-injection xenograft model. Taken together, the findings uncover a crucial role of Gal-1 in CAFs that orchestrates an inflammatory CSC niche supporting HCC progression and demonstrate that targeting Gal-1 could be a potential therapy for fibrosis-related HCC.

Peptide-Based Drug Predictions for Cancer Therapy Using Deep Learning.

Anticancer peptides (ACPs) are selective and toxic to cancer cells as new anticancer drugs. Identifying new ACPs is time-consuming and expensive to evaluate all candidates' anticancer abilities. To reduce the cost of ACP drug development, we collected the most updated ACP data to train a convolutional neural network (CNN) with a peptide sequence encoding method for initial in silico evaluation. Here we introduced PC6, a novel protein-encoding method, to convert a peptide sequence into a computational matrix, representing six physicochemical properties of each amino acid. By integrating data, encoding method, and deep learning model, we developed AI4ACP, a user-friendly web-based ACP distinguisher that can predict the anticancer property of query peptides and promote the discovery of peptides with anticancer activity. The experimental results demonstrate that AI4ACP in CNN, trained using the new ACP collection, outperforms the existing ACP predictors. The 5-fold cross-validation of AI4ACP with the new collection also showed that the model could perform at a stable level on high accuracy around 0.89 without overfitting. Using AI4ACP, users can easily accomplish an early-stage evaluation of unknown peptides and select potential candidates to test their anticancer activities quickly.

AI4AVP: an antiviral peptides predictor in deep learning approach with generative adversarial network data augmentation.

Motivation: Antiviral peptides (AVPs) from various sources suggest the possibility of developing peptide drugs for treating viral diseases. Because of the increasing number of identified AVPs and the advances in deep learning theory, it is reasonable to experiment with peptide drug design using in silico methods. Results: We collected the most up-to-date AVPs and used deep learning to construct a sequence-based binary classifier. A generative adversarial network was employed to augment the number of AVPs in the positive training dataset and enable our deep learning convolutional neural network (CNN) model to learn from the negative dataset. Our classifier outperformed other state-of-the-art classifiers when using the testing dataset. We have placed the trained classifiers on a user-friendly web server, AI4AVP, for the research community. Availability and implementation: AI4AVP is freely accessible at http://axp.iis.sinica.edu.tw/AI4AVP/; codes and datasets for the peptide GAN and the AVP predictor CNN are available at https://github.com/lsbnb/amp_gan and https://github.com/LinTzuTang/AI4AVP_predictor. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Identification of Genes Related to Cold Tolerance and Novel Genetic Markers for Molecular Breeding in Taiwan Tilapia (Oreochromis spp.) via Transcriptome Analysis.

Taiwan tilapia is one of the primary species used in aquaculture practices in Taiwan. However, as a tropical fish, it is sensitive to cold temperatures that can lead to high mortality rates during winter months. Genetic and broodstock management strategies using marker-assisted selection and breeding are the best tools currently available to improve seed varieties for tilapia species. The purpose of this study was to develop molecular markers for cold stress-related genes using digital gene expression analysis of next-generation transcriptome sequencing in Taiwan tilapia (Oreochromis spp.). We constructed and sequenced cDNA libraries from the brain, gill, liver, and muscle tissues of cold-tolerance (CT) and cold-sensitivity (CS) strains. Approximately 35,214,833,100 nucleotides of raw sequencing reads were generated, and these were assembled into 128,147 unigenes possessing a total length of 185,382,926 bp and an average length of 1446 bp. A total of 25,844 unigenes were annotated using five protein databases and Venny analysis, and 38,377 simple sequence repeats (SSRs) and 65,527 single nucleotide polymorphisms (SNPs) were identified. Furthermore, from the 38-cold tolerance-related genes that were identified using differential gene expression analysis in the four tissues, 13 microsatellites and 37 single nucleotide polymorphism markers were identified. The results of the genotype analysis revealed that the selected markers could be used for population genetics. In addition to the diversity assessment, one of the SNP markers was determined to be significantly related to cold-tolerance traits and could be used as a molecular marker to assist in the selection and verification of cold-tolerant populations. The specific genetic markers explored in this study can be used for the identification of genetic polymorphisms and cold tolerance traits in Taiwan tilapia, and they can also be used to further explore the physiological and biochemical molecular regulation pathways of fish that are involved in their tolerance to environmental temperature stress.

AI4AMP: an Antimicrobial Peptide Predictor Using Physicochemical Property-Based Encoding Method and Deep Learning.

Antimicrobial peptides (AMPs) are innate immune components that have recently stimulated considerable interest among drug developers due to their potential as antibiotic substitutes. AMPs are notable for their fundamental properties of microbial membrane structural interference and the biomedical applications of killing or suppressing microbes. New AMP candidates must be developed to oppose antibiotic resistance. However, the discovery of novel AMPs through wet-lab screening approaches is inefficient and expensive. The prediction model investigated in this study may help accelerate this process. We collected both the up-to-date AMP data set and unbiased negatives based on which the protein-encoding methods and deep learning model for AMPs were investigated. The external testing results indicated that our trained model achieved 90% precision, outperforming current methods. We implemented our model on a user-friendly web server, AI4AMP, to accurately predict the antimicrobial potential of a given protein sequence and perform proteome screening. IMPORTANCE Antimicrobial peptides (AMPs) are innate immune components that have aroused a great deal of interest among drug developers recently, as they may become a substitute for antibiotics. New candidates need to fight antibiotic resistance, while discovering novel AMPs through wet-lab screening approaches is inefficient and expensive. To accelerate the discovery of new AMPs, we both collected the up-to-date antimicrobial peptide data set and integrated the protein-encoding methods with a deep learning model. The trained model outperforms the current methods and is implemented into a user-friendly web server, AI4AMP, to accurately predict the antimicrobial properties of a given protein sequence and perform proteome screening. Author Video: An author video summary of this article is available.

Investigating the Transcriptomic and Expression Presence-Absence Variation Exist in Japanese Eel (Anguilla japonica), a Primitive Teleost.

The pan-genome was defined as the complete gene set across strains, and it is built upon genes displaying presence-absence variations (PAVs); the pan-transcriptome is defined by recalling the pan-genome. Indeed, a PAV is reflected from the expression presence-absence variation (ePAV). In this study, treated with androgen, eels, which are a primitive fish from the basal lineage of Teleost, with different ovarian developments were chosen and submitted to RAN-sequencing. Transcriptomes were the assembly against eel genome scaffolds; a pair was the unit (the same eel before and after treatment) to analyze DEGs (differentially expressed genes); the core, unique, or accessory genes were identified, and the list of DEGs was analyzed to investigate ePAV. The results suggest that there was ePAV in Japanese eel, and the ePAV of eel was analyzed by pathway enrichment. These results signify the importance of genetic differential expression on the variations of phenotypes by androgen, and a transcriptomic approach appears to enable extracting multiple layers of genomic data.

Androgenic Sensitivities and Ovarian Gene Expression Profiles Prior to Treatment in Japanese Eel (Anguilla japonica).

Androgens stimulate ovarian development in eels. Our previous report indicated a correlation between the initial (debut) ovarian status (determined by kernel density estimation (KDE), presented as a probability density of oocyte size) and the consequence of 17MT treatment (change in ovary). The initial ovarian status appeared to be an important factor influencing ovarian androgenic sensitivity. We postulated that the sensitivities of initial ovaries are correlated with their gene expression profiles. Japanese eels underwent operation to sample the initial ovarian tissues, and the samples were stored in liquid nitrogen. Using high-throughput next-generation sequencing (NGS) technology, ovarian transcriptomic data were mined and analyzed based on functional gene classification with cutoff-based differentially expressed genes (DEGs); the ovarian status was transformed into gene expression profiles globally or was represented by a set of gene list. Our results also implied that the initial ovary might be an important factor influencing the outcomes of 17MT treatments, and the genes related with neuronal activities or neurogenesis seemed to play an essential role in the positive effect.

Development of Disease-Resistance-Associated Microsatellite DNA Markers for Selective Breeding of Tilapia (Oreochromis spp.) Farmed in Taiwan.

There are numerous means to improve the tilapia aquaculture industry, and one is to develop disease resistance through selective breeding using molecular markers. In this study, 11 disease-resistance-associated microsatellite markers including 3 markers linked to hamp2, 4 linked to hamp1, 1 linked to pgrn2, 2 linked to pgrn1, and 1 linked to piscidin 4 (TP4) genes were established for tilapia strains farmed in Taiwan after challenge with Streptococcus inae. The correlation analysis of genotypes and survival revealed a total of 55 genotypes related to survival by the chi-square and Z-test. Although fewer markers were found in B and N2 strains compared with A strain, they performed well in terms of disease resistance. It suggested that this may be due to the low potency of some genotypes and the combinatorial arrangement between them. Therefore, a predictive model was built by the genotypes of the parental generation and the mortality rate of different combinations was calculated. The results show the same trend of predicted mortality in the offspring of three new disease-resistant strains as in the challenge experiment. The present findings is a nonkilling method without requiring the selection by challenge with bacteria or viruses and might increase the possibility of utilization of selective breeding using SSR markers in farms.

Gut Fecal Microbiota Transplant in a Mouse Model of Orthotopic Rectal Cancer.

The gut microbiota is reported to play an important role in carcinogenesis and the treatment of CRC. SW480 and SW620 colon cancer cells integrated with infrared fluorescent proteins were injected into the rectal submucosa of nude mice. In the subsequent 30 days, we observed tumor growth weekly using an in vivo imaging system. The bacterial solution was infused anally into the mice to perform bacterial transplant. Phosphate-buffered saline, Acinetobacter lwoffii, and Bifidobacterium longum solutions were infused individually. The 16S ribosomal DNA (rDNA) and polymerase chain reaction of murine feces were investigated to confirm the colonization of target bacteria. In the SW620 orthotopic xenograft rectal cancer model, 4 of 5 mice developed rectal cancer by 30 days after submucosal injection. In the SW480 orthotopic xenograft rectal cancer model, 2 of 6 mice developed rectal cancer by 30 days after submucosal injection. For the 16S rDNA analysis, the mice receiving the bacterial solution infusion demonstrated positive findings for A. lwoffii and B. longum. With the successful establishment of a mouse model of orthotopic rectal cancer and transplant of target bacteria, we can further explore the relationship between gut microbiota and CRC. The role of fecal microbiota transplant in the treatment and alleviation of adverse events of chemotherapy in CRC could be clarified in subsequent studies.