Comparative genomic analysis and optimization of astaxanthin production of Rhodotorula paludigena TL35-5 and Rhodotorula sampaioana PL61-2.

Astaxanthin is a powerful antioxidant known to enhance skin, cardiovascular, eye, and brain health. In this study, the genome insights and astaxanthin production of two newly isolated astaxanthin-producing yeasts (TL35-5 and PL61-2) were evaluated and compared. Based on their phenotypic and genotypic characteristics, TL35-5 and PL61-2 were identified as basidiomycetous yeasts belonging to Rhodotorula paludigena and Rhodotorula sampaioana, respectively. To optimize astaxanthin production, the effects of cultural medium composition and cultivation conditions were examined. The optimal conditions for astaxanthin production in R. paludigena TL35-5 involved cultivation in AP medium containing 10 g/L glucose as the sole carbon source, supplemented with 1.92 g/L potassium nitrate, pH 6.5, and incubation at 20°C for 3 days with shaking at 200 rpm. For R. sampaioana PL61-2, the optimal medium composition for astaxanthin production consisted of AP medium with 40 g/L glucose, supplemented with 0.67 g/L urea, pH 7.5, and the fermentation was carried out at 20°C for 3 days with agitating at 200 rpm. Under their optimal conditions, R. paludigena TL35-5 and R. sampaioana PL61-2 gave the highest astaxanthin yields of 3.689 ± 0.031 and 4.680 ± 0.019 mg/L, respectively. The genome of TL35-5 was 20,982,417 bp in length, with a GC content of 64.20%. A total of 6,789 protein-encoding genes were predicted. Similarly, the genome of PL61-2 was 21,374,169 bp long, with a GC content of 64.88%. It contained 6,802 predicted protein-encoding genes. Furthermore, all essential genes involved in astaxanthin biosynthesis, including CrtE, CrtYB, CrtI, CrtS, and CrtR, were identified in both R. paludigena TL35-5 and R. sampaioana PL61-2, providing evidence for their ability to produce astaxanthin.

Robust and consistent biomarker candidates identification by a machine learning approach applied to pancreatic ductal adenocarcinoma metastasis.

BACKGROUND: Machine Learning (ML) plays a crucial role in biomedical research. Nevertheless, it still has limitations in data integration and irreproducibility. To address these challenges, robust methods are needed. Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive cancer with low early detection rates and survival rates, is used as a case study. PDAC lacks reliable diagnostic biomarkers, especially metastatic biomarkers, which remains an unmet need. In this study, we propose an ML-based approach for discovering disease biomarkers, apply it to the identification of a PDAC metastatic composite biomarker candidate, and demonstrate the advantages of harnessing data resources. METHODS: We utilised primary tumour RNAseq data from five public repositories, pooling samples to maximise statistical power and integrating data by correcting for technical variance. Data were split into train and validation sets. The train dataset underwent variable selection via a 10-fold cross-validation process that combined three algorithms in 100 models per fold. Genes found in at least 80% of models and five folds were considered robust to build a consensus multivariate model. A random forest model was constructed using selected genes from the train dataset and tested in the validation set. We also assessed the goodness of prediction by recalibrating a model using only the validation data. The biological context and relevance of signals was explored through enrichment and pathway analyses using QIAGEN Ingenuity Pathway Analysis and GeneMANIA. RESULTS: We developed a pipeline that can detect robust signatures to build composite biomarkers. We tested the pipeline in PDAC, exploiting transcriptomics data from different sources, proposing a composite biomarker candidate comprised of fifteen genes consistently selected that showed very promising predictive capability. Biological contextualisation revealed links with cancer progression and metastasis, underscoring their potential relevance. All code is available in GitHub. CONCLUSION: This study establishes a robust framework for identifying composite biomarkers across various disease contexts. We demonstrate its potential by proposing a plausible composite biomarker candidate for PDAC metastasis. By reusing data from public repositories, we highlight the sustainability of our research and the wider applications of our pipeline. The preliminary findings shed light on a promising validation and application path.

Characterization and phylogenetic analysis of the complete chloroplast genome of Curcuma comosa and C. latifolia.

Members of the Curcuma genus, a crop in the Zingiberaceae, are widely utilized rhizomatous herbs globally. There are two distinct species, C. comosa Roxb. and C. latifolia Roscoe, referred to the same vernacular name "Wan Chak Motluk" in Thai. C. comosa holds economic importance and is extensively used as a Thai traditional medicine due to its phytoestrogenic properties. However, its morphology closely resembles that of C. latifolia, which contains zederone, a compound known for its hepatotoxic effects. They are often confused, which may affect the quality, efficacy and safety of the derived herbal materials. Thus, DNA markers were developed for discriminating C. comosa from C. latifolia. This study focused on analyzing core DNA barcode regions, including rbcL, matK, psbA-trnH spacer and ITS2, of the authentic C. comosa and C. latifolia species. As a result, no variable nucleotides in core DNA barcode regions were observed. The complete chloroplast (cp) genome was introduced to differentiate between the two species. The comparison revealed that the cp genomes of C. comosa and C. latifolia were 162,272 and 162,289 bp, respectively, with a total of 133 identified genes. The phylogenetic analysis revealed that C. comosa and C. latifolia exhibited a very close relationship with other Curcuma species. The cp genome of C. comosa and C. latifolia were identified for the first time, providing valuable insights for species identification and evolutionary research within the Zingiberaceae family.

Development of a machine learning model for systematics of Aspergillus section Nigri using synchrotron radiation-based fourier transform infrared spectroscopy.

Aspergillus section Nigri (black aspergilli) fungi are economically important food spoilage agents. Some species in this section also produce harmful mycotoxins in food. However, it is remarkably difficult to identify this fungal group at the species level using morphological and chemical characteristics. The molecular approach for classification is preferable; however, it is time-consuming, making it inappropriate for rapid testing of large numbers of samples. To address this, we explored synchrotron radiation-based Fourier transform infrared microspectroscopy (SR-FTIR) as a rapid method for obtaining data suitable for species classification. SR-FTIR data were obtained from the mycelia/conidia of 22 black aspergilli species. The Convolutional Neural Network (CNN) approach, a supervised deep learning algorithm, was used with SR-FTIR data to classify black aspergilli at the species level. A subset of the data was used to train the CNN model, and the model classification performance was evaluated using the validation data subsets. The model demonstrated a 95.97% accuracy in species classification on the testing (blind) data subset. The technique presented herein could be an alternative method for identifying problematic black aspergilli in the food industry.

Mol-Zero-GAN: zero-shot adaptation of molecular generative adversarial network for specific protein targets.

Drug discovery is a process that finds new potential drug candidates for curing diseases and is also vital to improving the wellness of people. Enhancing deep learning approaches, e.g., molecular generation models, increases the drug discovery process's efficiency. However, there is a problem in this field in creating drug candidates with desired properties such as the quantitative estimate of druglikeness (QED), synthetic accessibility (SA), and binding affinity (BA), and there is a challenge for training a generative model for specific protein targets that has less pharmaceutical data. In this research, we present Mol-Zero-GAN, a framework that aims to solve the problem based on Bayesian optimization (BO) to find the model optimal weights' singular values, factorized by singular value decomposition, and generate drug candidates with desired properties with no additional data. The proposed framework can produce drugs with the desired properties on protein targets of interest by optimizing the model's weights. Our framework outperforms the state-of-the-art methods sharing the same objectives. Mol-Zero-GAN is publicly available at https://github.com/cucpbioinfo/Mol-Zero-GAN.

Diversity, astaxanthin production, and genomic analysis of Rhodotorula paludigena SP9-15.

Astaxanthin is a carotenoid known for its powerful antioxidant properties. This study focused on isolating yeast strains capable of producing astaxanthin from flower and fruit samples collected in Thailand. Out of 115 isolates, 11 strains were identified that produced astaxanthin. Molecular identification techniques revealed that these isolates belonged to two species: Rhodotorula paludigena (5 isolates) and Rhodosporidiobolus ruineniae (6 isolates). Whole-genome analysis of one representative strain, R. paludigena SP9-15, identified putative candidate astaxanthin synthesis-associated genes, such as CrtE, CrtYB, CrtI, CrtS, CrtR, CrtW, CrtO, and CrtZ. High-performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC-MS) confirmed astaxanthin production. Further optimization of astaxanthin production was carried out by investigating the effects of various factors on the growth rate and astaxanthin production. The optimal conditions were 40 g/L glucose as a carbon source, pH 7.5, and cultivation at 25 °C with 200 rpm for 3 days. Under these conditions, R. paludigena SP9-15 synthesized biomass of 11.771 ± 0.003 g/L, resulting in astaxanthin with a content of 0.558 ± 0.018 mg/g DCW (dry cell weight), an astaxanthin yield of 6.565 ± 0.238 mg/L, and astaxanthin productivity of 2.188 ± 0.069 g/L/day. These findings provide insights into astaxanthin production using red yeast strains from Thailand and highlight the potential of R. paludigena SP9-15 for further application.

CHO-produced RBD-Fc subunit vaccines with alternative adjuvants generate immune responses against SARS-CoV-2.

Subunit vaccines feature critical advantages over other vaccine platforms such as stability, price, and minimal adverse effects. To maximize immunological protection of subunit vaccines, adjuvants are considered as main components that are formulated within the subunit vaccine. They can modulate adverse effects and enhance immune outcomes. However, the most suitable formulation providing the best immunological outcomes and safety are still under investigation. In this report, we combined recombinant RBD with human IgG1 Fc to create an RBD dimer. This fusion protein was expressed in CHO and formulated with alternative adjuvants with different immune activation including Montanide ISA51, Poly (I:C), and MPLA/Quil-A® as potential vaccine candidate formulations. Using the murine model, a potent induction of anti-RBD IgG antibodies in immunized mice sera were observed. IgG subclass analyses (IgG1/IgG2a) illustrated that all adjuvanted formulations could stimulate both Th1 and Th2-type immune responses in particular Poly (I:C) and MPLA/Quil-A®, eliciting greater balance. In addition, Montanide ISA51-formulated RBD-Fc vaccination provided a promising level of neutralizing antibodies against live wild-type SARS-CoV-2 in vitro followed by Poly (I:C) and MPLA/Quil-A®, respectively. Also, mice sera from adjuvanted formulations could strongly inhibit RBD:ACE2 interaction. This study offers immunogenicity profiles, forecasted safety based on Vaccine-associated enhanced disease (VAED) caused by Th1-skewed immunity, and neutralizing antibody analysis of candidates of RBD-Fc-based subunit vaccine formulations to obtain an alternative subunit vaccine formulation against SARS-CoV-2.

Identifying molecular targets of Aspiletrein-derived steroidal saponins in lung cancer using network pharmacology and molecular docking-based assessments.

Lung cancer is one of the leading cancers and causes of cancer-related deaths worldwide. Due to its high prevalence and mortality rate, its clinical management remains a significant challenge. Previously, the in vitro anticancer activity of Aspiletrein A, a steroid and a saponin from Aspidistra letreae, against non-small cell lung cancer (NSCLC) cells was reported. However, the anticancer molecular mechanism of other Aspiletreins from A. letreae remains unknown. Using in silico network pharmacology approaches, the targets of Aspiletreins were predicted using the Swiss Target Prediction database. In addition, key mediators in NSCLC were obtained from the Genetic databases. The compound-target interacting networks were constructed using the STRING database and Cytoscape, uncovering potential targets, including STAT3, VEGFA, HSP90AA1, FGF2, and IL2. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated that several pathways were highly relevant to cancer pathogenesis. Additionally, molecular docking and molecular dynamic analyses revealed the interaction between key identified targets and Aspiletreins, including hydrogen bonding and Van der Waals interaction. This study provides potential targets of Aspiletreins in NSCLC, and its approach of integrating network pharmacology, bioinformatics, and molecular docking is a powerful tool for investigating the mechanism of new drug targets on a specific disease.

Development of Fenofibrate/Randomly Methylated ß-Cyclodextrin-Loaded Eudragit® RL 100 Nanoparticles for Ocular Delivery.

Fenofibrate (FE) has been shown to markedly reduce the progression of diabetic retinopathy and age-related macular degeneration in clinical trials and animal models. Owing to the limited aqueous solubility of FE, it may hamper ocular bioavailability and result in low efficiency to treat such diseases. To enhance the solubility of FE, water-soluble FE/cyclodextrin (CD) complex formation was determined by a phase-solubility technique. Randomly methylated-ß-CD (RMßCD) exhibited the best solubility and the highest complexation efficiency (CE) for FE. Additionally, water-soluble polymers (i.e., hydroxypropyl methyl cellulose and polyvinyl alcohol [PVA]) enhanced the solubility of FE/RMßCD complexes. Solid- and solution-state characterizations were performed to elucidate and confirm the formation of inclusion FE/RMßCD complex. FE-loaded Eudragit® nanoparticle (EuNP) dispersions and suspensions were developed. The physicochemical properties (i.e., pH, osmolality, viscosity, particle size, size distribution, and zeta potential) were within acceptable ranges. Moreover, in vitro mucoadhesion, in vitro release, and in vitro permeation studies revealed that the FE-loaded EuNP eye drop suspensions had excellent mucoadhesive properties and sustained FE release. The hemolytic activity, hen's egg test on chorioallantoic membrane assay, and in vitro cytotoxicity test showed that the FE formulations had low hemolytic activity, were cytocompatible, and were moderately irritable to the eyes. In conclusion, PVA-stabilized FE/RMßCD-loaded EuNP eye drop suspensions were successfully developed, warranting further in vivo testing.

Development of a DNA barcode library of plants in the Thai Herbal Pharmacopoeia and Monographs for authentication of herbal products.

Traditional herbal medicine has long been practiced as a method of health care in many countries worldwide. The usage of herbal products has been increasing and is expected to continue to do so in the future. However, admixture and adulteration are concerns regarding the quality of herbal medicine, including its safety and efficacy. We aimed to develop a reference DNA barcode library of plants listed in the Thai Herbal Pharmacopoeia (THP) and Monographs of Selected Thai Materia Medica (TMM) (n = 101 plant species) using four core barcode regions, namely, the ITS2, matK, rbcL and trnH-psbA intergenic spacer regions, for authentication of the plant origin of raw materials and herbal products. Checking sequences from samples obtained from local markets and the Thai Food and Drug Administration (Thai FDA) against our digital reference DNA barcode system revealed the authenticity of eighteen out of twenty tested samples as claimed on their labels. Two samples, no. 3 and 13, were not Cyanthillium cinereum (L.) H.Rob. and Pueraria candollei Wall. ex Benth. as claimed, respectively. They were recognized as Emilia sonchifolia (L.) DC. and Butea superba (Roxb.), respectively. Hence, it is important for the Thai FDA or regulatory agencies to immediately initiate strict enforcement for the development of pharmacopoeial standards as well as revisions or modifications of available regulatory guidelines and to implement close monitoring for the quality control of herbal products in terms of authentication before they enter the herbal market. The centralized digital reference DNA barcode database developed here could play a very important role in monitoring or checking the authenticity of medicinal plants.

Is Hyperdermium Congeneric with Ascopolyporus? Phylogenetic Relationships of Ascopolyporus spp. (Cordycipitaceae, Hypocreales) and a New Genus Neohyperdermium on Scale Insects in Thailand.

During surveys of insect pathogenic fungi (IPF) in Thailand, fungi associated with scale insects and plants were found to represent five new species of the genus Ascopolyporus in Cordycipitaceae. Their macroscopic features resembled both Hyperdermium and Ascopolyporus. Morphological comparisons with the type and known Ascopolyporus and Hyperdermium species and phylogenetic evidence from a multigene dataset support the appointment of a new species of Ascopolyporus. Moreover, the data also revealed that the type species of Hyperdermium, H. caulium, is nested within Ascopolyporus, suggesting that Hyperdermium is congeneric with Ascopolyporus. The specimens investigated here differ from other Ascopolyporus species by phenotypic characters including size and color of stromata. Phylogenetic analyses of combined LSU, TEF1, RPB1 and RPB2 sequences strongly support the notion that these strains are distinct from known species of Ascopolyporus, and are proposed as Ascopolyporus albus, A. galloides, A. griseoperitheciatus, A. khaoyaiensis and A. purpuratus. Neohyperdermium gen. nov. is introduced for other species originally assigned to Hyperdermium and Cordyceps occurring on scale insects and host plants as epiphytes, accommodating two new combinations of Hyperdermium pulvinatum and Cordyceps piperis.

A multi-epitope chimeric protein elicited a strong antibody response and partial protection against Edwardsiella ictaluri in Nile tilapia.

Edwardsiella ictaluri infects several fish species and protection of the all the susceptible fish hosts from the pathogen using a monovalent vaccine is impossible because the species is composed of host-based genotypes that are genetic, serological and antigenic heterogenous. Here, immunoinformatic approach was employed to design a cross-immunogenic chimeric EiCh protein containing multi-epitopes. The chimeric EiCh protein is composed of 11 B-cell epitopes and 7 major histocompatibility complex class II epitopes identified from E. ictaluri immunogenic proteins previously reported. The 49.32 kDa recombinant EiCh protein was expressed in vitro in Escherichia coli BL-21 (DE3) after which inclusion bodies were successfully solubilized and refolded. Ab initio protein modelling revealed secondary and tertiary structures. Secondary structure was confirmed by circular dichroism spectroscopy. Antigenicity of the chimeric EiCh protein was exhibited by strong reactivity with serum from striped catfish and Nile tilapia experimentally infected with E. ictaluri. Furthermore, immunogenicity of the chimeric EiCh protein was investigated in vivo in Nile tilapia juveniles and it was found that the protein could strongly induce production of specific antibodies conferring agglutination activity and partially protected Nile tilapia juveniles with a relative survival percentage (RPS) of 42%. This study explored immunoinformatics as reverse vaccinology approach in vaccine design for aquaculture to manage E. ictaluri infections.

Resurfacing receptor binding domain of Colicin N to enhance its cytotoxic effect on human lung cancer cells.

Colicin N (ColN) is a bacteriocin secreted by Escherichia coli (E. coli) to kill other Gram-negative bacteria by forcefully generating ion channels in the inner membrane. In addition to its bactericidal activity, ColN have been reported to selectively induce apoptosis in human lung cancer cells via the suppression of integrin modulated survival pathway. However, ColN showed mild toxicity against human lung cancer cells which could be improved for further applications. The protein resurfacing strategy was chosen to engineer ColN by extensive mutagenesis at solvent--exposed residues on ColN. The highly accessible Asp and Glu on wild-type ColN (ColNWT) were replaced by Lys to create polycationic ColN (ColN+12). Previous studies have shown that increase of positive charges on proteins leads to the enhancement of mammalian cell penetration as well as increased interaction with negatively charged surface of cancer cells. Those solvent--exposed residues of ColN were identified by Rosetta and AvNAPSA (Average number of Neighboring Atoms Per Side-chain Atom) approaches. The findings revealed that the structural features and stability of ColN+12 determined by circular dichroism were similar to ColNWT. Furthermore, the toxicity of ColN+12 was cancer -selective. Human lung cancer cells, H460 and H23, were sensitive to ColN but human dermal papilla cells were not. ColN+12 also showed more potent toxicity than ColNWT in cancer cells. This confirmed that polycationic resurfacing method has enabled us to improve the anticancer activity of ColN towards human lung cancer cells.

Development and Qualification of a Physiologically Based Pharmacokinetic Model of Finasteride and Minoxidil Following Scalp Application.

In this study, we aimed to develop and qualify a PBPK model for scalp application using two drugs with marked differences in physicochemical properties and PK profiles. The parameters related to scalp physiology, drug PK, and formulations were incorporated into a Multi-Phase and Multi-Layer (MPML) Mechanistic Dermal Absorption (MechDermA) model within the Simcyp® Simulator V17. The finasteride PBPK model was linked to its effect on dihydrotestosterone (DHT) levels in plasma and scalp using an indirect response model. Predicted PK (and PD for finasteride) profiles and parameters were compared against the clinically reported data and justified by visual predictive checks and two-fold error criteria for model verification. The PBPK/PD model for finasteride reasonably demonstrated an ability to predict its respective PK and PD profiles, and parameters following scalp application under various clinical scenarios. Using the same scalp physiological input parameters, the minoxidil PBPK model was then developed and satisfactorily qualified with independent clinical datasets. Collectively, these results suggested that the established PBPK model may have broader utility for other topical formulations intended for scalp application.

Chemosensitizing activity of peptide from Lentinus squarrosulus (Mont.) on cisplatin-induced apoptosis in human lung cancer cells.

The limitations of cisplatin, a standard chemotherapy for lung cancer, have been documented with serious adverse effects and drug resistance. To address the need for novel therapy, this study firstly reveals the potential of peptide from Lentinus squarrosulus (Mont.) as a chemotherapeutic adjuvant for cisplatin treatment. The purified peptide from L. squarrosulus aqueous extracts was obtained after eluting with 0.4 M NaCl through FPLC equipped with anion exchange column. Preincubation for 24 h with 5 µg/mL of the peptide at prior to treatment with 5 µM cisplatin significantly diminished %cell viability in various human lung cancer cells but not in human dermal papilla and proximal renal cells. Flow cytometry indicated the augmentation of cisplatin-induced apoptosis in lung cancer cells pretreated with peptide from L. squarrosulus. Preculture with the peptide dramatically inhibited colony formation in lung cancer cells derived after cisplatin treatment. Strong suppression on integrin-mediated survival was evidenced with the diminution of integrins (ß1, ß3, ß5, α5, αV) and down-stream signals (p-FAK/FAK, p-Src/Src, p-Akt/Akt) consequence with alteration of p53, Bax, Blc-2 and Mcl-1 in cisplatin-treated lung cancer cells preincubated with peptide from L. squarrosulus. These results support the development of L. squarrosulus peptide as a novel combined chemotherapy with cisplatin for lung cancer treatment.

Four Novel Phenanthrene Derivatives with α-Glucosidase Inhibitory Activity from Gastrochilus bellinus.

Four new phenanthrene derivatives, gastrobellinols A-D (1-4), were isolated from the methanolic extract of Gastrochilus bellinus (Rchb.f.) Kuntze, along with eleven known phenolic compounds including agrostophyllin (5), agrostophyllidin (6), coniferyl aldehyde (7), 4-hydroxybenzaldehyde (8), agrostophyllone (9), gigantol (10), 4-(methoxylmethyl)phenol (11), syringaldehyde (12), 1-(4'-hydroxybenzyl)-imbricartin (13), 6-methoxycoelonin (14), and imbricatin (15). Their structures were determined by spectroscopic methods. Each isolate was evaluated for α-glucosidase inhibitory activity. Compounds 1, 2, 3, 7, 9, 13, and 15 showed higher activity than the drug acarbose. Gastrobellinol C (3) exhibited the strongest α-glucosidase inhibition with an IC50 value of 45.92 µM. A kinetic study of 3 showed competitive inhibition on the α-glucosidase enzyme. This is the first report on the phytochemical constituents and α-glucosidase inhibitory activity of G. bellinus.

Using machine learning-based analytics of daily activities to identify modifiable risk factors for falling in Parkinson's disease.

BACKGROUND: Although risk factors that lead to falling in Parkinson's disease (PD) have been previously studied, the established predictors are mostly non-modifiable. A novel method for fall risk assessment may provide more insight into preventable high-risk activities to reduce future falls. OBJECTIVES: To explore the prediction of falling in PD patients using a machine learning-based approach. METHOD: 305 PD patients, with or without a history of falls within the past month, were recruited. Data including clinical demographics, medications, and balance confidence, scaled by the 16-item Activities-Specific Balance Confidence Scale (ABC-16), were entered into the supervised machine learning models using XGBoost to explore the prediction of fallers/recurrent fallers in two separate models. RESULTS: 99 (32%) patients were fallers and 58 (19%) were recurrent fallers. The accuracy of the model to predict falls was 72% (p = 0.001). The most important factors were item 7 (sweeping the floor), item 5 (reaching on tiptoes), and item 12 (walking in a crowded mall) in the ABC-16 scale, followed by disease stage and duration. When recurrent falls were analysed, the models had higher accuracy (81%, p = 0.02). The strongest predictors of recurrent falls were item 12, 5, and 10 (walking across parking lot), followed by disease stage and current age. CONCLUSION: Our machine learning-based study demonstrated that predictors of falling combined demographics of PD with environmental factors, including high-risk activities that require cognitive attention and changes in vertical and lateral orientations. This enables physicians to focus on modifiable factors and appropriately implement fall prevention strategies for individual patients.

KITSUNE: A Tool for Identifying Empirically Optimal K-mer Length for Alignment-Free Phylogenomic Analysis.

Genomic DNA is the best "unique identifier" for organisms. Alignment-free phylogenomic analysis, simple, fast, and efficient method to compare genome sequences, relies on looking at the distribution of small DNA sequence of a particular length, referred to as k-mer. The k-mer approach has been explored as a basis for sequence analysis applications, including assembly, phylogenetic tree inference, and classification. Although this approach is not novel, selecting the appropriate k-mer length to obtain the optimal resolution is rather arbitrary. However, it is a very important parameter for achieving the appropriate resolution for genome/sequence distances to infer biologically meaningful phylogenetic relationships. Thus, there is a need for a systematic approach to identify the appropriate k-mer from whole-genome sequences. We present K-mer-length Iterative Selection for UNbiased Ecophylogenomics (KITSUNE), a tool for assessing the empirically optimal k-mer length of any given set of genomes of interest for phylogenomic analysis via a three-step approach based on (1) cumulative relative entropy (CRE), (2) average number of common features (ACF), and (3) observed common features (OCF). Using KITSUNE, we demonstrated the feasibility and reliability of these measurements to obtain empirically optimal k-mer lengths of 11, 17, and ∼34 from large genome datasets of viruses, bacteria, and fungi, respectively. Moreover, we demonstrated a feature of KITSUNE for accurate species identification for the two de novo assembled bacterial genomes derived from error-prone long-reads sequences, and for a published yeast genome. In addition, KITSUNE was used to identify the shortest species-specific k-mer accurately identifying viruses. KITSUNE is freely available at https://github.com/natapol/kitsune.

In silico analysis for factors affecting anti-malarial penetration into red blood cells.

BACKGROUND: Malaria is a parasitic disease that produces significant infection in red blood cells. The objective of this study is to investigate the relationships between factors affecting the penetration of currently available anti-malarials into red blood cells. METHODS: Fifteen anti-malarial drugs listed in the third edition of the World Health Organization malaria treatment guidelines were enrolled in the study. Relationship analysis began with the prioritization of the physicochemical properties of the anti-malarials to create a multivariate linear regression model that correlates the red blood cell penetration. RESULTS: It was found that protein binding was significantly correlated with red blood cell penetration, with a negative coefficient. The next step was repeated analysis to find molecular descriptors that influence protein binding. The coefficients of the number of rotating bonds and the number of aliphatic hydrocarbons are negative, as opposed to the positive coefficients of the number of hydrogen bonds and the number of aromatic hydrocarbons. The p-value was less than 0.05. CONCLUSIONS: Anti-malarials with a small number of hydrogen bonds and aromatic hydrocarbons, together with a high number of rotatable bonds and aliphatic hydrocarbons, may have a higher tendency to penetrate the red blood cells.

Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data.

Metabolite profiles from biological samples suffer from both technical variations and subject-specific variants. To improve the quality of metabolomics data, conventional data processing methods can be employed to remove technical variations. These methods do not consider sources of subject variation as separate factors from biological factors of interest. This can be a significant issue when performing quantitative metabolomics in clinical trials or screening for a potential biomarker in early-stage disease, because changes in metabolism or a desired-metabolite signal are small compared to the total metabolite signals. As a result, inter-individual variability can interfere subsequent statistical analyses. Here, we propose an additional data processing step using linear mixed-effects modelling to readjust an individual metabolite signal prior to multivariate analyses. Published clinical metabolomics data was used to demonstrate and evaluate the proposed method. We observed a substantial reduction in variation of each metabolite signal after model fitting. A comparison with other strategies showed that our proposed method contributed to improved classification accuracy, precision, sensitivity and specificity. Moreover, we highlight the importance of patient metadata as it contains rich information of subject characteristics, which can be used to model and normalize metabolite abundances. The proposed method is available as an R package lmm2met.