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.

Polyphosphate metabolism by purple non-sulfur bacteria and its possible application on photo-microbial fuel cell.

A purple non-sulfur bacterium, Rhodopseudomonas palustris G11, was isolated from an activated sludge plant that treats domestic wastewater. This isolation resulted in the effective accumulation of polyphosphate in cells upon reaching the stationary growth phase. However, when the carbon and/or energy source were/was removed, this bacterium released intracellular polyphosphate or poly-ß-hydroxybutyrate to obtain energy to grow or maintain its growth. Furthermore, a novel photo-microbial fuel cell (PMFC) design was proposed. The unique capability of purple non-sulfur bacteria to capture light energy for polyphosphate accumulation was maximized. After R. palustris G11 accumulated considerable polyphosphate and was transferred to a fresh medium, the PMFC system exhibited a maximum voltage of approximately 0.03 V undt illumination. The chemical oxygen demand removal efficiency, Coulomb efficiency, and power density were 95.8%, 0.62%, and 0.15 mW/m2, respectively. The test microorganisms converted most of the light energy in growth and caused the low power production. The microorganisms grew slowly and produced less power under dark conditions than under light illumination. However, these microorganisms used the previously stored polyphosphate or poly-ß-hydroxybutyrate for electricity production when they were incubated in a growth-insufficient condition. This novel concept can be improved and optimized in the future for new PMFC applications, such as rechargeable cells, to treat wastewater and restore energy simultaneously.

Subset selection of high-depth next generation sequencing reads for de novo genome assembly using MapReduce framework.

BACKGROUND: Recent progress in next-generation sequencing technology has afforded several improvements such as ultra-high throughput at low cost, very high read quality, and substantially increased sequencing depth. State-of-the-art high-throughput sequencers, such as the Illumina MiSeq system, can generate ~15 Gbp sequencing data per run, with >80% bases above Q30 and a sequencing depth of up to several 1000x for small genomes. Illumina HiSeq 2500 is capable of generating up to 1 Tbp per run, with >80% bases above Q30 and often >100x sequencing depth for large genomes. To speed up otherwise time-consuming genome assembly and/or to obtain a skeleton of the assembly quickly for scaffolding or progressive assembly, methods for noise removal and reduction of redundancy in the original data, with almost equal or better assembly results, are worth studying. RESULTS: We developed two subset selection methods for single-end reads and a method for paired-end reads based on base quality scores and other read analytic tools using the MapReduce framework. We proposed two strategies to select reads: MinimalQ and ProductQ. MinimalQ selects reads with minimal base-quality above a threshold. ProductQ selects reads with probability of no incorrect base above a threshold. In the single-end experiments, we used Escherichia coli and Bacillus cereus datasets of MiSeq, Velvet assembler for genome assembly, and GAGE benchmark tools for result evaluation. In the paired-end experiments, we used the giant grouper (Epinephelus lanceolatus) dataset of HiSeq, ALLPATHS-LG genome assembler, and QUAST quality assessment tool for comparing genome assemblies of the original set and the subset. The results show that subset selection not only can speed up the genome assembly but also can produce substantially longer scaffolds. AVAILABILITY: The software is freely available at https://github.com/moneycat/QReadSelector.

CSNK1E/CTNNB1 are synthetic lethal to TP53 in colorectal cancer and are markers for prognosis.

Two genes are called synthetic lethal (SL) if their simultaneous mutations lead to cell death, but each individual mutation does not. Targeting SL partners of mutated cancer genes can kill cancer cells specifically, but leave normal cells intact. We present an integrated approach to uncovering SL pairs in colorectal cancer (CRC). Screening verified SL pairs using microarray gene expression data of cancerous and normal tissues, we first identified potential functionally relevant (simultaneously differentially expressed) gene pairs. From the top-ranked pairs, ~20 genes were chosen for immunohistochemistry (IHC) staining in 171 CRC patients. To find novel SL pairs, all 169 combined pairs from the individual IHC were synergistically correlated to five clinicopathological features, e.g. overall survival. Of the 11 predicted SL pairs, MSH2-POLB and CSNK1E-MYC were consistent with literature, and we validated the top two pairs, CSNK1E-TP53 and CTNNB1-TP53 using RNAi knockdown and small molecule inhibitors of CSNK1E in isogenic HCT-116 and RKO cells. Furthermore, synthetic lethality of CSNK1E and TP53 was verified in mouse model. Importantly, multivariate analysis revealed that CSNK1E-P53, CTNNB1-P53, MSH2-RB1, and BRCA1-WNT5A were independent prognosis markers from stage, with CSNK1E-P53 applicable to early-stage and the remaining three throughout all stages. Our findings suggest that CSNK1E is a promising target for TP53-mutant CRC patients which constitute ~40% to 50% of patients, while to date safety regarding inhibition of TP53 is controversial. Thus the integrated approach is useful in finding novel SL pairs for cancer therapeutics, and it is readily accessible and applicable to other cancers.

Successful enrichment of rarely found Candidatus Anammoxoglobus propionicus from leachate sludge.

Bacteria that mediate the anaerobic oxidation of ammonium (anammox) have been detected in natural ecosystems, as well as various wastewater treatment systems. In this study, sludge from a particular landfill leachate anaerobic treatment system was selected as the incubation seed for anammox microorganism enrichment owing to its possible anammox activity. Transmission electron microscopy observation, denaturing gradient gel electrophoresis analysis, and cloning/sequencing techniques were applied to identify the diversity of anammox microorganisms throughout the incubation. During the early stage of operation, the diversity of anammox microorganisms was similar to the original complex microbes in the seed sludge. However, as incubation time increased, the anammox microorganism diversity within the system that was originally dominated by Candidatus (Ca.) Brocadia sp. was replaced by Ca. Anammoxoglobus propionicus. The domination of Ca. Anammoxoglobus propionicus produced a stable removal of ammonia (70 mg-N/l) and nitrite (90 mg-N/l), and the total nitrogen removal efficiency was maintained at nearly 95%. The fluorescence in situ hybridization results showed that Ca. Anammoxoglobus propionicus was successfully enriched from 1.8 ± 0.6% initially to 65 ± 5% after 481 days of operation. Therefore, the present results demonstrated the feasibility of enriching Ca. Anammoxoglobus propionicus from leachate sludge, even though the original cell count was extremely low. Application of this seldom found anammox organism could offer an alternative to current ammonia-nitrogen treatment.

Inferring coregulation of transcription factors and microRNAs in breast cancer.

Both transcription factors (TFs) and microRNAs (miRNAs) regulate gene expression. TFs activate or suppress the initiation of the transcription process and miRNAs regulate mRNAs post-transcriptionally, thus forming a temporally ordered regulatory event. Ectopic expression of key transcriptional regulators and/or miRNAs has been shown to be involved in various tumors. Therefore, uncovering the coregulation of TFs and miRNAs in human cancers may lead to the discovery of novel therapeutics. We introduced a two-stage learning fuzzy method to model TF-miRNA coregulation using both genomic data and verified regulatory relationships. In Stage 1, a learning (adaptive) fuzzy inference system (ANFIS) combines two sequence alignment features of TF and target by learning from verified TF-target pairs into a sequence matching score. Next, a non-learning FIS incorporates a sequence alignment score and a correlation score from paired TF-target gene expression to output a Stage 1 fuzzy score to infer whether a TF-target regulation exists. For significant TF-target pairs, in Stage 2, similar to Stage 1, we first infer whether a miRNA regulates each common target by an ANFIS, which incorporates their sequences and known miRNA-target relationships to output a sequence score. Next, an FIS incorporates the Stage 1 fuzzy score, Stage 2 sequence score and gene expression correlation score of a miRNA-target pair to determine whether TF-miRNA coregulation exists. We collected 54 (8) TF-miRNA-target triples validated in ER-positive (ER-negative) breast cancer cell lines in the same article, and they were used as positives. Negative examples were constructed for Stage 1 (Stage 2) by pairing TFs (miRNAs) with human housekeeping genes found in the literature; both positives and negatives were used to train ANFISs in the training step. This two-stage fizzy algorithm was applied to predict 54 (8) TF-miRNA coregulation triples in ER-positive (ER-negative) human breast cancer cell lines, and resulted in true-positive rates of 0.55 (0.74) and 0.57 (0.75) using 3-fold and n-fold cross validations (CVs), respectively. False-positive rate bound was 0.07 (0.13) for ER-positive (ER-negative) breast cancer using both 3-fold and n-fold CVs. Interestingly, among the 62 coregulatroy triples from ER-positive/negative breast cancer cells, about 72% have TF- and coregulatory miRNA expression simultaneously greater or less than the corresponding medians, while in the remaining 28% of TFs and their coregulatory miRNAs are conversely expressed. The proposed fuzzy algorithm performed well in identification of TF-miRNA coregulation triples in human breast cancer. After being trained by the corresponding verified coregulatory triples and genomic data, this algorithm can be applied to uncover novel coregulation in other cancers in the future.

WITHDRAWN: Bioaugmentation strategies to improve cellulolytic and hydrogen-producing characteristics of CSTR intermittent fed with vegetable kitchen waste and napiergrass.

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Improved candidate biomarker detection based on mass spectrometry data using the Hilbert-Huang transform.

Mass spectrometry biomarker discovery may assist patient's diagnosis in time and realize the characteristics of new diseases. Our previous work built a preprocess method called HHTmass which is capable of removing noise, but HHTmass only a proof of principle to be peak detectable and did not tested for peak reappearance rate and used on medical data. We developed a modified version of biomarker discovery method called Enhance HHTMass (E-HHTMass) for MALDI-TOF and SELDI-TOF mass spectrometry data which improved old HHTMass method by removing the interpolation and the biomarker discovery process. E-HHTMass integrates the preprocessing and classification functions to identify significant peaks. The results show that most known biomarker can be found and high peak appearance rate achieved comparing to MSCAP and old HHTMass2. E-HHTMass is able to adapt to spectra with a small increasing interval. In addition, new peaks are detected which can be potential biomarker after further validation.