Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection.

Protein crystallization is crucial for biology, but the steps involved are complex and demanding in terms of external factors and internal structure. To save on experimental costs and time, the tendency of proteins to crystallize can be initially determined and screened by modeling. As a result, this study created a new pipeline aimed at using protein sequence to predict protein crystallization propensity in the protein material production stage, purification stage and production of crystal stage. The newly created pipeline proposed a new feature selection method, which involves combining Chi-square (${\chi }^{2}$) and recursive feature elimination together with the 12 selected features, followed by a linear discriminant analysisfor dimensionality reduction and finally, a support vector machine algorithm with hyperparameter tuning and 10-fold cross-validation is used to train the model and test the results. This new pipeline has been tested on three different datasets, and the accuracy rates are higher than the existing pipelines. In conclusion, our model provides a new solution to predict multistage protein crystallization propensity which is a big challenge in computational biology.

Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding.

Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher selectivity. Due to ACP identification being highly lab-limited, expensive and lengthy, a computational method is proposed to predict ACPs from sequence information in this study. The process includes the input of the peptide sequences, feature extraction in terms of ordinal encoding with positional information and handcrafted features, and finally feature selection. The whole model comprises of two modules, including deep learning and machine learning algorithms. The deep learning module contained two channels: bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN). Light Gradient Boosting Machine (LightGBM) was used in the machine learning module. Finally, this study voted the three models' classification results for the three paths resulting in the model ensemble layer. This study provides insights into ACP prediction utilizing a novel method and presented a promising performance. It used a benchmark dataset for further exploration and improvement compared with previous studies. Our final model has an accuracy of 0.7895, sensitivity of 0.8153 and specificity of 0.7676, and it was increased by at least 2% compared with the state-of-the-art studies in all metrics. Hence, this paper presents a novel method that can potentially predict ACPs more effectively and efficiently. The work and source codes are made available to the community of researchers and developers at https://github.com/khanhlee/acp-ope/.

A biosynthetic aspartate N-hydroxylase performs successive oxidations by holding intermediates at a site away from the catalytic center.

Nitrosuccinate is a biosynthetic building block in many microbial pathways. The metabolite is produced by dedicated L-aspartate hydroxylases that use NADPH and molecular oxygen as co-substrates. Here, we investigate the mechanism underlying the unusual ability of these enzymes to perform successive rounds of oxidative modifications. The crystal structure of Streptomyces sp. V2 L-aspartate N-hydroxylase outlines a characteristic helical domain wedged between two dinucleotide-binding domains. Together with NADPH and FAD, a cluster of conserved arginine residues forms the catalytic core at the domain interface. Aspartate is found to bind in an entry chamber that is close to but not in direct contact with the flavin. It is recognized by an extensive H-bond network that explains the enzyme's strict substrate-selectivity. A mutant designed to create steric and electrostatic hindrance to substrate binding disables hydroxylation without perturbing the NADPH oxidase side-activity. Critically, the distance between the FAD and the substrate is far too long to afford N-hydroxylation by the C4a-hydroperoxyflavin intermediate whose formation is confirmed by our work. We conclude that the enzyme functions through a catch-and-release mechanism. L-aspartate slides into the catalytic center only when the hydroxylating apparatus is formed. It is then re-captured by the entry chamber where it waits for the next round of hydroxylation. By iterating these steps, the enzyme minimizes the leakage of incompletely oxygenated products and ensures that the reaction carries on until nitrosuccinate is formed. This unstable product can then be engaged by a successive biosynthetic enzyme or undergoes spontaneous decarboxylation to produce 3-nitropropionate, a mycotoxin.

Low non-relapse mortality and good haematological and renal responses after autologous haematopoietic stem cell transplantation in multiple myeloma patients with renal insufficiency at transplant: A prospective Société Francophone de Greffe de Moelle-Thérapie Cellulaire observational study.

High-dose melphalan followed by autologous haematopoietic stem cell transplantation is widely used in newly diagnosed multiple myeloma (MM) patients as upfront therapy. However, the safety and efficacy of transplantation in patients with renal insufficiency (RI) are controversial. We followed a multicentre (16 SFGM-TC centres) prospective cohort of 50 newly diagnosed MM patients with a serum creatinine clearance of <40 mL/min at transplantation. Patients received a recommended dose of melphalan of 140 mg/m2. The primary end-point was the non-relapse mortality at Day 100. One death occurred during the first 100 days post-transplant. The median time to neutrophil engraftment was 12 days and to platelet engraftment was 13 days. The haematological response improved in 69% of patients, with best responses from partial response (PR) to very good partial response (VGPR) (10%), from PR to complete response (CR)/stringent complete response (sCR) (16%), from VGPR to CR/sCR (39%) and from CR to sCR (2%). At 2 years, the overall survival was 84%, the progression-free survival was 70% and the cumulative incidence of relapse was 20%. The renal response improved in 59% of patients, with the best renal responses post-transplant being minimal (9%), partial (2%) and complete (48%). Autologous transplantation was safe and effective in myeloma patients with RI at transplant.

Deficiency of BMAL1 promotes ROS generation and enhances IgE-dependent degranulation in mast cells.

BACKGROUND: Bmal1 (Brain and muscle arnt-like, or Arntl) is a bHLH/PAS domain transcription factor central to the transcription/translation feedback loop of the circadian clock. Mast cells are crucial for effector functions in allergic reaction and their activity follows a circadian rhythm. However, the functional roles of Bmal1 in mast cells remain to be determined. PURPOSE: This study aimed to elucidate the specific roles of Bmal1 in IgE-dependent mast cell degranulation. RESULTS: IgE-dependent degranulation was enhanced in bone marrow-derived mast cells (BMMCs) derived from Bmal1-deficient mice (Bmal1-KO mice) compared to that in BMMCs derived from wild-type mice (WT mice) in the absence of 2-Mercaptoethanol (2-ME) in culture. Mast cell-deficient KitW-sh mice reconstituted with Bmal1-KO BMMCs showed more robust passive cutaneous anaphylactic (PCA) reactions, an in vivo model of IgE-dependent mast cell degranulation, than KitW-sh mice reconstituted with WT BMMCs. In the absence of 2-ME in culture, the mRNA expression of the anti-oxidative genes NF-E2-related factor 2 (Nrf2), superoxide dismutase 2 (SOD2), and heme oxygenase-1 (HO-1) was lower and reactive oxygen species (ROS) generation was higher in Bmal1-KO BMMCs than in WT BMMCs at steady state. The IgE-dependent ROS generation and degranulation were enhanced in Bmal1-KO BMMCs compared to WT BMMCs in the absence of 2-ME in culture. The addition of 2-ME into the culture abrogated or weakened the differences in anti-oxidative gene expression, ROS generation, and IgE-dependent degranulation between WT and Bmal1-KO BMMCs. CONCLUSION: The current findings suggest that Bmal1 controls the expression of anti-oxidative genes in mast cells and Bmal1 deficiency enhanced IgE-dependent degranulation associated with promotion of ROS generation. Thus, Bmal1 may function as a key molecule that integrates redox homeostasis and effector functions in mast cells.

Constructing B─N─P Bonds in Ultrathin Holey g-C3N4 for Regulating the Local Chemical Environment in Photocatalytic CO2 Reduction to CO.

The lack of intrinsic active sites for photocatalytic CO2 reduction reaction (CO2RR) and fast recombination rate of charge carriers are the main obstacles to achieving high photocatalytic activity. In this work, a novel phosphorus and boron binary-doped graphitic carbon nitride, highly porous material that exhibits powerful photocatalytic CO2 reduction activity, specifically toward selective CO generation, is disclosed. The coexistence of Lewis-acidic and Lewis-basic sites plays a key role in tuning the electronic structure, promoting charge distribution, extending light-harvesting ability, and promoting dissociation of excitons into active carriers. Porosity and dual dopants create local chemical environments that activate the pyridinic nitrogen atom between the phosphorus and boron atoms on the exposed surface, enabling it to function as an active site for CO2RR. The P-N-B triad is found to lower the activation barrier for reduction of CO2 by stabilizing the COOH reaction intermediate and altering the rate-determining step. As a result, CO yield increased to 22.45 µmol g-1 h-1 under visible light irradiation, which is ≈12 times larger than that of pristine graphitic carbon nitride. This study provides insights into the mechanism of charge carrier dynamics and active site determination, contributing to the understanding of the photocatalytic CO2RR mechanism.

Costs associated with invasive Scedosporium and Lomentospora prolificans infections: a case-control study.

BACKGROUND: Little is known about the short- and long-term healthcare costs of invasive Scedosporium/Lomentospora prolificans infections, particularly in patient groups without haematological malignancy. This study investigated excess index hospitalization costs and cumulative costs of these infections. The predictors of excess cost and length of stay (LOS) of index hospitalization were determined. These estimates serve as valuable inputs for cost-effectiveness models of novel antifungal agents. METHODS: A retrospective case-control study was conducted at six Australian hospitals. Cases of proven/probable invasive Scedosporium/L. prolificans infections between 2011 and 2021 (n = 34) were matched with controls (n = 66) by predefined criteria. Cost data were retrieved from activity-based costing systems and analysis was performed from the Australian public hospital perspective. All costs were presented in 2022 Australian dollars (AUD). Median regression analysis was used to adjust excess costs of index hospitalization whereas cumulative costs up to 1.5 years follow-up were estimated using interval-partitioned survival probabilities. RESULTS: Invasive Scedosporium/L. prolificans infections were independently associated with an adjusted median excess cost of AUD36 422 (P = 0.003) and LOS of 16.27 days (P < 0.001) during index hospitalization. Inpatient stay was the major cost driver (42.7%), followed by pharmacy cost, of which antifungal agents comprised 23.8% of the total cost. Allogeneic haematopoietic stem cell transplant increased the excess cost (P = 0.013) and prolonged LOS (P < 0.001) whereas inpatient death within ≤28 days reduced both cost (P = 0.001) and LOS (P < 0.001). The median cumulative cost increased substantially to AUD203 292 over 1.5 years in cases with Scedosporium/L. prolificans infections. CONCLUSIONS: The economic burden associated with invasive Scedosporium/L. prolificans infections is substantial.

mCNN-ETC: identifying electron transporters and their functional families by using multiple windows scanning techniques in convolutional neural networks with evolutionary information of protein sequences.

In the past decade, convolutional neural networks (CNNs) have been used as powerful tools by scientists to solve visual data tasks. However, many efforts of convolutional neural networks in solving protein function prediction and extracting useful information from protein sequences have certain limitations. In this research, we propose a new method to improve the weaknesses of the previous method. mCNN-ETC is a deep learning model which can transform the protein evolutionary information into image-like data composed of 20 channels, which correspond to the 20 amino acids in the protein sequence. We constructed CNN layers with different scanning windows in parallel to enhance the useful pattern detection ability of the proposed model. Then we filtered specific patterns through the 1-max pooling layer before inputting them into the prediction layer. This research attempts to solve a basic problem in biology in terms of application: predicting electron transporters and classifying their corresponding complexes. The performance result reached an accuracy of 97.41%, which was nearly 6% higher than its predecessor. We have also published a web server on http://bio219.bioinfo.yzu.edu.tw, which can be used for research purposes free of charge.

Improving nitrogen content in soil and lemon balm (Melissa officinalis L.) yield by purple nonsulfur bacteria Rhodopseudomonas palustris in two consecutive seasons.

The study was conducted to assess the effects of nitrogen (N)-fixing purple nonsulfur bacteria (PNSB) Rhodopseudomonas palustris TLS06, VNW02, VNW64, and VNS89 on soil fertility, N uptake, essential oil (EO) content, growth, and yield of lemon balm. The experiment followed a completely randomized block design with 9 treatments and 3 replications. The treatments consisted of (i) applying 100% N as the recommended fertilizer rate (RFR), (ii) applying 85% N as RFR, (iii) applying 70% N as RFR, (iv) applying 55% N as RFR, (v) the treatment ii combined with N-PNSB, (vi) the treatment iii combined with N-PNSB, (vii) the treatment iv combined with N-PNSB, (viii) 0% as RFR combined with N-PNSB, and (ix) 0% N as RFR. The results showed that applying N-PNSB increased the plant height, and the number of primary branches in both seasons. In addition, the treatment without N fertilizer combined with N-PNSB increased stem leaf biomass by 41.2 and 50.3% in both seasons as compared with the treatment without neither N fertilizer nor N-PNSB. For soil properties, among treatments without N fertilizer, the treatment with N-PNSB increased concentrations of NH4+, soluble P, and exchangeable K+ by 41.3, 41.4, and 26.8%, respectively, as compared with the treatment without N-PNSB at the end of the second season. Applying 85% N as RFR combined with N-PNSB had a greater yield by 5.78-11.8% as compared with the treatment with 100% N as RFR, and a greater EO content by 23% as compared with the treatment with 85% N as RFR.

Outcomes of CMML patients undergoing allo-HCT are significantly worse compared to MDS-a study of the CMWP of the EBMT.

Although CMML since long has been separated from MDS, many studies continue to evaluate the outcomes of both diseases after hematopoietic cell transplantation (allo-HCT) together. Data evaluating outcomes of a large CMML cohort after allo-HCT compared to MDS are limited. We aim to compare outcomes of CMML to MDS patients who underwent allo-HCT between 2010 and 2018. Patients ≥18 years with CMML and MDS undergoing allo-HCT reported to the EBMT registry were analyzed. Progression to AML before allo-HCT was an exclusion criterion. Overall survival (OS), progression/relapse-free survival (PFS), relapse incidence (including progression) (REL), and non-relapse mortality (NRM) were evaluated in univariable and multivariable (MVA) Cox proportional hazard models including interaction terms between disease and confounders. In total, 10832 patients who underwent allo-HCT were included in the study, there were a total of 1466 CMML, and 9366 MDS. The median age at time of allo-HCT in CMML (median 60.5, IQR 54.3-65.2 years) was significantly higher than in the MDS cohort (median 58.8, IQR 50.2-64.5 years; p < .001). A significantly higher percentage of CMML patients were male (69.4%) compared to MDS (61.2%; p < .001). There were no clinically meaningful differences in the distribution of Karnofsky score, Sorror HCT-CI score at allo-HCT, and donor type, between the CMML and MDS patients. RIC platforms were utilized in 63.9% of CMML allo-HCT, and in 61.4% of MDS patients (p = .08). In univariable analyses, we found that OS, PFS, and REL were significantly worse in CMML when compared with MDS (all p < .0001), whereas no significant difference was observed in NRM (p = .77). In multivariable analyses, the HR comparing MDS versus CMML for OS was 0.81 (95% CI, 0.74-0.88, p < .001), PFS 0.76 (95% CI 0.70-0.82, p < .001), relapse 0.66 (95% CI 0.59-0.74, p < .001), and NRM 0.87 (95% CI 0.78-0.98, p = .02), respectively. The association between baseline variables and outcome was found to be similar in MDS and CMML (all interaction p > .05) except for a decreasing trend over time of the risk of relapse in CMML (HR allo-HCT per year later 0.94, 95% CI 0.90-0.98), whereas no such trend was observed in MDS (HR 1.00, 95% CI 0.98-1.02). The poor outcome observed for CMML could be related to variables not measured in this study or to factors inherent to the disease itself. This study demonstrates that outcomes of CMML patients after allo-HCT are significantly worse compared to MDS. The results of this study may contribute to future recommendations for allo-HCT in CMML patients.

Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine.

With the rising demand for in vitro fertilization (IVF) cycles, there is a growing need for innovative techniques to optimize procedure outcomes. One such technique is time-lapse system (TLS) for embryo incubation, which minimizes environmental changes in the embryo culture process. TLS also significantly advances predicting embryo quality, a crucial determinant of IVF cycle success. However, the current subjective nature of embryo assessments is due to inter- and intra-observer subjectivity, resulting in highly variable results. To address this challenge, reproductive medicine has gradually turned to artificial intelligence (AI) to establish a standardized and objective approach, aiming to achieve higher success rates. Extensive research is underway investigating the utilization of AI in TLS to predict multiple outcomes. These studies explore the application of popular AI algorithms, their specific implementations, and the achieved advancements in TLS. This review aims to provide an overview of the advances in AI algorithms and their particular applications within the context of TLS and the potential challenges and opportunities for further advancements in reproductive medicine.

Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation.

PURPOSE: To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data. METHODS: This retrospective study utilized a dataset of 1908 blastocyst embryos. The dataset includes ploidy status, morphokinetic features, morphology grades, and 11 clinical variables. Six machine learning (ML) models including Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost (ADA), and Light Gradient-Boosting Machine (LGBM) were trained to predict ploidy status probabilities across three distinct datasets: high-grade embryos (HGE, n = 1107), low-grade embryos (LGE, n = 364), and all-grade embryos (AGE, n = 1471). The model's performance was interpreted using XAI, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) techniques. RESULTS: The mean maternal age was 38.5 ± 3.85 years. The Random Forest (RF) model exhibited superior performance compared to the other five ML models, achieving an accuracy of 0.749 and an AUC of 0.808 for AGE. In the external test set, the RF model achieved an accuracy of 0.714 and an AUC of 0.750 (95% CI, 0.702-0.796). SHAP's feature impact analysis highlighted that maternal age, paternal age, time to blastocyst (tB), and day 5 morphology grade significantly impacted the predictive model. In addition, LIME offered specific case-ploidy prediction probabilities, revealing the model's assigned values for each variable within a finite range. CONCLUSION: The model highlights the potential of using XAI algorithms to enhance ploidy prediction, optimize embryo selection as patient-centric consultation, and provides reliability and transparent insights into the decision-making process.

The potency of a liquid biofertilizer containing bacterial strains of Rhodopseudomonas spp. on recovery of soil properties damaged by Al3+ and Fe2+ toxins and enhancement of rice yield in acid sulfate soil.

In the Mekong Delta Vietnam, rice is heavily affected by Al3+ and Fe2+ ions appearing in local acid sulfate soils (AAS). Therefore, the current study was carried out to assess the efficacy of a liquid biofertilizer (LB) containing nitrogen-fixing and phosphorus-solubilizing bacterial strains of Rhodopseudomonas spp. on remediation of soil characteristics and improvements of rice uptakes, growth, and yield. The experiment was designed in a randomized block design with nine treatments and four replications in an ASS. The results have shown that the LB application could contribute to the remediation of soil properties, including an increase in concentrations of NH4+ by 12.9%-19.4%, soluble P by 25.7%-42.6%, total N uptake by 40.7-64.0 kg ha-1 and total P uptake by 5.60-12.6 kg ha-1, and a decrease in concentrations of toxins, such as Al3+ by 12.1%-19.7% and Fe2+ by 16.6%-19.0%, compared to the treatment with the farmer-based fertilization. Thereby, grain yield was improved by 31.9%-32.2% with the LB versus the treatments without the bacteria and by 9.5%-11.1% compared to the commercial biofertilizer treatments. The application of LB reduced 25% N and 50% P of the recommendation versus the farmers' fertilization and improved performance of rice growth and yield cultivated on ASS which suffered from Al3+ and Fe2+ ions.

The current study has introduced the potential of the Rhodopseudomonas palustris TLS06, VNW02, VNW64, and VNS89 strains in performance as a bioremediator and a biofertilizer. The strains have shown their ability to recover acid sulfate soils, which had damaged the yield of rice plants due to high concentrations of Al³⁺ and Fe²⁺ ions. The work has delivered a biological approach to improve acid sulfate soil fertility and rice productivity in Vietnam and in other parts of the world, which have similar conditions, to achieve sustainable agriculture and food security.

Interaction of SARS-CoV-2 with host cells and antibodies: experiment and simulation.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the devastating global COVID-19 pandemic announced by WHO in March 2020. Through unprecedented scientific effort, several vaccines, drugs and antibodies have been developed, saving millions of lives, but the fight against COVID-19 continues as immune escape variants of concern such as Delta and Omicron emerge. To develop more effective treatments and to elucidate the side effects caused by vaccines and therapeutic agents, a deeper understanding of the molecular interactions of SARS-CoV-2 with them and human cells is required. With special interest in computational approaches, we will focus on the structure of SARS-CoV-2 and the interaction of its spike protein with human angiotensin-converting enzyme-2 (ACE2) as a prime entry point of the virus into host cells. In addition, other possible viral receptors will be considered. The fusion of viral and human membranes and the interaction of the spike protein with antibodies and nanobodies will be discussed, as well as the effect of SARS-CoV-2 on protein synthesis in host cells.

Doxycycline pharmacokinetics and tissue depletion in striped catfish (Pagasianodon hypophthalmus) after oral administration.

The pharmacokinetics and residue depletion of doxycycline (DOX) in striped catfish (Pagasianodon hypophthalmus) after oral dosage were investigated. The pharmacokinetic experiment was conducted in an aquarium, while the experiment of residue depletion was performed in both an aquarium and earth ponds. Medicated feed was administered orally using the gavage method at a dosage of 20 mg/kg body weight. Blood, liver, and kidney from medicated fish samples were collected. In the depletion experiments, fish were fed medicated feed for five consecutive days at a dosage of 20 mg/kg body weight, with samples collected during and after medication. The concentrations of DOX were quantified using an LC-MS/MS system. The pharmacokinetics parameters of DOX in striped catfish included the absorption rate constant (ka), absorption half-life (T1/2abs), maximal plasma concentration (Cmax), time to maximal plasma concentration (Tmax), and area under the plasma concentration-time curve from time 0 to 96 h (AUC0-96 h) which were 0.12 h-1, 5.68 h, 1123.45 ng/mL, 8.19 h, and 25,018 ng/mL/h, respectively. Residue depletion results indicated that the withdrawal times of DOX in muscle (with skin) from fish kept in the aquarium were slightly longer than that in fish raised in earth ponds, corresponding to 194 degree-days compared with 150 degree-days. In conclusion, administration of DOX at the dosage of 20 mg/kg body weight can be used for treatment of bacterial infections in striped catfish, and a withdrawal time of 5 days at 29.4°C will ensure consumer food safety due to the rapid depletion of DOX from muscle and skin.

Multi-Omics Analysis Reveals the IFI6 Gene as a Prognostic Indicator and Therapeutic Target in Esophageal Cancer.

The role of the IFI6 gene has been described in several cancers, but its involvement in esophageal cancer (ESCA) remains unclear. This study aimed to identify novel prognostic indicators for ESCA-targeted therapy by investigating IFI6's expression, epigenetic mechanisms, and signaling activities. We utilized public data from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) to analyze IFI6's expression, clinical characteristics, gene function, pathways, and correlation with different immune cells in ESCA. The TIMER2.0 database was employed to assess the pan-cancer expression of IFI6, while UALCAN was used to examine its expression across tumor stages and histology subtypes. Additionally, the KEGG database helped identify related pathways. Our findings revealed 95 genes positively correlated and 15 genes negatively correlated with IFI6 in ESCA. IFI6 was over-expressed in ESCA and other cancers, impacting patient survival and showing higher expression in tumor tissues than normal tissues. IFI6 was also correlated with CD4+ T cells and B cell receptors (BCRs), both essential in immune response. GO Biological Process (GO BP) enrichment analysis indicated that IFI6 was primarily associated with the Type I interferon signaling pathway and the defense response to viruses. Intriguingly, KEGG pathway analysis demonstrated that IFI6 and its positively correlated genes in ESCA were mostly linked to the Cytosolic DNA-sensing pathway, which plays a crucial role in innate immunity and viral defense, and the RIG-I-like receptor (RLR) signaling pathway, which detects viral infections and activates immune responses. Pathways related to various viral infections were also identified. It is important to note that our study relied on online databases. Given that ESCA consists of two distinct subgroups (ESCC and EAC), most databases combine them into a single category. Future research should focus on evaluating IFI6 expression and its impact on each subgroup to gain more specific insights. In conclusion, inhibiting IFI6 using targeted therapy could be an effective strategy for treating ESCA considering its potential as a biomarker and correlation with immune cell factors.

Efficiency of freeze- and spray-dried microbial preparation as active dried starter culture in kombucha fermentation.

BACKGROUND: Kombucha is a widely consumed fermented beverage produced by fermenting sweet tea with a symbiotic culture of bacteria and yeast (SCOBY). The dynamic nature of microbial communities in SCOBY may pose challenges to production scale-up due to unpredictable variations in microbial composition. Using identified starter strains is a novel strategy to control microorganism composition, thereby ensuring uniform fermentation quality across diverse batches. However, challenges persist in the cultivation and maintenance of these microbial strains. This study examined the potential of microencapsulated kombucha fermentation starter cultures, specifically Komagataeibacter saccharivorans, Levilactobacillus brevis and Saccharomyces cerevisiae, through spray-drying and freeze-drying. RESULTS: Maltodextrin and gum arabic-maltodextrin were employed as carrier agents. Our results revealed that both spray-dried and freeze-dried samples adhered to physicochemical criteria, with low moisture content (2.18-7.75%) and relatively high solubility (65.75-87.03%) which are appropriate for food application. Freeze-drying demonstrated greater effectiveness in preserving bacterial strain viability (88.30-90.21%) compared to spray drying (74.92-78.66%). Additionally, the freeze-dried starter strains demonstrated similar efficacy in facilitating kombucha fermentation, compared to the SCOBY group. The observations included pH reduction, acetic acid production, α-amylase inhibition and elevated total polyphenol and flavonoid content. Moreover, the biological activity, including antioxidant potential and in vitro tyrosinase inhibition activity, was enhanced in the same pattern. The freeze-dried strains exhibited consistent kombucha fermentation capabilities over a three-month preservation, regardless of storage temperature at 30 or 4 °C. CONCLUSION: These findings highlight the suitability of freeze-dried starter cultures for kombucha production, enable microbial composition control, mitigate contamination risks and ensure consistent product quality. © 2024 Society of Chemical Industry.

Leveraging transformers-based language models in proteome bioinformatics.

In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.

IS-Seq: a bioinformatics pipeline for integration sites analysis with comprehensive abundance quantification methods.

BACKGROUND: Integration site (IS) analysis is a fundamental analytical platform for evaluating the safety and efficacy of viral vector based preclinical and clinical Gene Therapy (GT). A handful of groups have developed standardized bioinformatics pipelines to process IS sequencing data, to generate reports, and/or to perform comparative studies across different GT trials. Keeping up with the technological advances in the field of IS analysis, different computational pipelines have been published over the past decade. These pipelines focus on identifying IS from single-read sequencing or paired-end sequencing data either using read-based or using sonication fragment-based methods, but there is a lack of a bioinformatics tool that automatically includes unique molecular identifiers (UMI) for IS abundance estimations and allows comparing multiple quantification methods in one integrated pipeline. RESULTS: Here we present IS-Seq a bioinformatics pipeline that can process data from paired-end sequencing of both old restriction sites-based IS collection methods and new sonication-based IS retrieval systems while allowing the selection of different abundance estimation methods, including read-based, Fragment-based and UMI-based systems. CONCLUSIONS: We validated the performance of IS-Seq by testing it against the most popular  analytical workflow available in the literature (INSPIIRED) and using different scenarios. Lastly, by performing extensive simulation studies and a comprehensive wet-lab assessment of our IS-Seq pipeline we could show that in clinically relevant scenarios, UMI quantification provides better accuracy than the currently most widely used sonication fragment counts as a method for IS abundance estimation.

Molecular basis for substrate recognition and septum cleavage by AtlA, the major N-acetylglucosaminidase of Enterococcus faecalis.

The cleavage of septal peptidoglycan at the end of cell division facilitates the separation of the two daughter cells. The hydrolases involved in this process (called autolysins) are potentially lethal enzymes that can cause cell death; their activity, therefore, must be tightly controlled during cell growth. In Enterococcus faecalis, the N-acetylglucosaminidase AtlA plays a predominant role in cell separation. atlA mutants form long cell chains and are significantly less virulent in the zebrafish model of infection. The attenuated virulence of atlA mutants is underpinned by a limited dissemination of bacterial chains in the host organism and a more efficient uptake by phagocytes that clear the infection. AtlA has structural homologs in other important pathogens, such as Listeria monocytogenes and Salmonella typhimurium, and therefore represents an attractive model to design new inhibitors of bacterial pathogenesis. Here, we provide a 1.45 Å crystal structure of the E. faecalis AtlA catalytic domain that reveals a closed conformation of a conserved ß-hairpin and a complex network of hydrogen bonds that bring two catalytic residues to the ideal distance for an inverting mechanism. Based on the model of the AtlA-substrate complex, we identify key residues critical for substrate recognition and septum cleavage during bacterial growth. We propose that this work will provide useful information for the rational design of specific inhibitors targeting this enterococcal virulence factor and its orthologs in other pathogens.