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Effects of unstable β-PheRS on food avoidance, growth, and development are suppressed by the appetite hormone CCHa2

Amino acyl-tRNA synthetases perform diverse non-canonical functions aside from their essential role in charging tRNAs with their cognate amino acid. The phenylalanyl-tRNA synthetase (PheRS/FARS) is an α2β2 tetramer that is needed for charging the tRNAPhe for its translation activity. Fragments of the α-subunit have been shown to display an additional, translation-independent, function that activates growth and proliferation and counteracts Notch signalling. Here we show in Drosophila that overexpressing the β-subunit in the context of the complete PheRS leads to larval roaming, food avoidance, slow growth, and a developmental delay that can last several days and even prevents pupation. These behavioural and developmental phenotypes are induced by PheRS expression in CCHa2+ and Pros+ cells. Simultaneous expression of β-PheRS, α-PheRS, and the appetite-inducing CCHa2 peptide rescued these phenotypes, linking this β-PheRS activity to the appetite-controlling pathway. The fragmentation dynamic of the excessive β-PheRS points to β-PheRS fragments as possible candidate inducers of these phenotypes. Because fragmentation of human FARS has also been observed in human cells and mutations in human β-PheRS (FARSB) can lead to problems in gaining weight, Drosophila β-PheRS can also serve as a model for the human phenotype and possibly also for obesity.

Transcriptomic analysis of Chaetoceros muelleri in response to different nitrogen concentrations reveals the activation of pathways to enable efficient nitrogen uptake

Nitrogen is the principal nutrient deficiency that increases lipids and carbohydrate content in diatoms but negatively affects biomass production. Marine diatom Chaetoceros muelleri is characterized by lipid and carbohydrate accumulation under low nitrogen concentration without affecting biomass. To elucidate the molecular effects of nitrogen concentrations, we performed an RNA-seq analysis of C. muelleri grown under four nitrogen concentrations (3.53 mM, 1.76 mM, 0.44 mM, and 0.18 mM of NaNO3). This research revealed that changes in global transcription in C. muelleri are differentially expressed by nitrogen concentration. "Energetic metabolism", "Carbohydrate metabolism" and "Lipid metabolism" pathways were identified as the most upregulated by N deficiency. Due to N limitation, alternative pathways to self-supply nitrogen employed by microalgal cells were identified. Additionally, nitrogen limitation decreased chlorophyll content and caused a greater response at the transcriptional level with a higher number of unigenes differentially expressed. By contrast, the highest N concentration (3.53 mM) recorded the lowest number of differentially expressed genes. Amt1, Nrt2, Fad2, Skn7, Wrky19, and Dgat2 genes were evaluated by RT-qPCR. In conclusion, C. muelleri modify their metabolic pathways to optimize nitrogen utilization and minimize nitrogen losses. On the other hand, the assembled transcriptome serves as the basis for metabolic engineering focused on improving the quantity and quality of the diatom for biotechnological applications. However, proteomic and metabolomic analysis is also required to compare gene expression, protein, and metabolite accumulation.

Several secondary metabolite gene clusters in the genomes of ten Penicillium spp. raise the risk of multiple mycotoxin occurrence in chestnuts

Penicillium spp. produce a great variety of secondary metabolites, including several mycotoxins, on food substrates. Chestnuts represent a favorable substrate for Penicillium spp. development. In this study, the genomes of ten Penicillium species, virulent on chestnuts, were sequenced and annotated: P. bialowiezense. P. pancosmium, P. manginii, P. discolor, P. crustosum, P. palitans, P. viridicatum, P. glandicola, P. taurinense and P. terrarumae. Assembly size ranges from 27.5 to 36.8 Mb and the number of encoded genes ranges from 9,867 to 12,520. The total number of predicted biosynthetic gene clusters (BGCs) in the ten species is 551. The most represented families of BGCs are non ribosomal peptide synthase (191) and polyketide synthase (175), followed by terpene synthases (87). Genome-wide collections of gene phylogenies (phylomes) were reconstructed for each of the newly sequenced Penicillium species allowing for the prediction of orthologous relationships among our species, as well as other 20 annotated Penicillium species available in the public domain. We investigated in silico the presence of BGCs for 10 secondary metabolites, including 5 mycotoxins, whose production was validated in vivo through chemical analyses. Among the clusters present in this set of species we found andrastin A and its related cluster atlantinone A, mycophenolic acid, patulin, penitrem A and the cluster responsible for the synthesis of roquefortine C/glandicoline A/glandicoline B/meleagrin. We confirmed the presence of these clusters in several of the Penicillium species conforming our dataset and verified their capacity to synthesize them in a chestnut-based medium with chemical analysis. Interestingly, we identified mycotoxin clusters in some species for the first time, such as the andrastin A cluster in P. flavigenum and P. taurinense, and the roquefortine C cluster in P. nalgiovense and P. taurinense. Chestnuts proved to be an optimal substrate for species of Penicillium with different mycotoxigenic potential, opening the door to risks related to the occurrence of multiple mycotoxins in the same food matrix.

Research progress on prediction of RNA-protein binding sites in the past five years

Accurately predicting RNA-protein binding sites is essential to gain a deeper comprehension of the protein-RNA interactions and their regulatory mechanisms, which are fundamental in gene expression and regulation. However, conventional biological approaches to detect these sites are often costly and time-consuming. In contrast, computational methods for predicting RNA protein binding sites are both cost-effective and expeditious. This review synthesizes already existing computational methods, summarizing commonly used databases for predicting RNA protein binding sites. In addition, applications and innovations of computational methods using traditional machine learning and deep learning for RNA protein binding site prediction during 2018-2023 are presented. These methods cover a wide range of aspects such as effective database utilization, feature selection and encoding, innovative classification algorithms, and evaluation strategies. Exploring the limitations of existing computational methods, this paper delves into the potential directions for future development. DeepRKE, RDense, and DeepDW all employ convolutional neural networks and long and short-term memory networks to construct prediction models, yet their algorithm design and feature encoding differ, resulting in diverse prediction performances.

Revealing the mechanism of post-harvest processing on rose quality based on dynamic changes in water content, enzyme activity, volatile and non-volatile metabolites

Existing studies on post-harvest processing of edible roses have mainly focused on processing techniques and physicochemical properties of the final dried products, with limited studies on how changes in metabolites during processing affect the quality of these products. This study investigated changes in water content and status, enzyme activity, phenolic compounds, and volatile and non-volatile compounds during processing and revealed the mechanisms by which post-harvest processing (drying without blanching (WBD) and drying with blanching (BD)) affects the quality of dried roses by establishing their correlations. Results showed that the blanching reduced the relative content of free water and water activity, thus reducing the subsequent drying time and enzyme activity. The BD method caused higher levels of phenolic compounds than the WBD method in terms of gallic acid, ellagic acid, epicatechin, and quercetin. The OPLS-DA analysis identified 6 differential volatiles out of 72 detected volatiles, contributing to the unique aroma of dried roses by activating olfactory receptors through hydrogen bonding and hydrophobic interactions. 58 differential metabolites were screened from 964 non-volatile metabolites. KEGG pathway analysis revealed that the changes in volatile and non-volatile metabolites induced by different processing methods were due to the effect of blanching on glutathione and fatty acid metabolism. These findings provide a comprehensive understanding of how post-harvest processing affects the quality of dried roses.

Identification of therapeutic drug target of Shigella Flexneri serotype X through subtractive genomic approach and in-silico screening based on drug repurposing

Shigellosis, induced by Shigella flexneri, constitutes a significant health burden in developing nations, particularly impacting socioeconomically disadvantaged communities. Designated as the second most prevalent cause of diarrheal illness by the World Health Organization (WHO), it precipitates an estimated 212,000 fatalities annually. Within the spectrum of S. flexneri strains, serotype X is notably pervasive and resilient, yet its comprehensive characterization remains deficient. The present investigation endeavors to discern potential pharmacological targets and repurpose existing drug compounds against S. flexneri serotype X. Employing the framework of subtractive genomics, the study interrogates the reference genome of S. flexneri Serotype X (strain 2,002,017; UP000001884) to delineate its proteome into categories of non-homologous, non-paralogous, essential, virulent, and resistant constituents, thereby facilitating the identification of therapeutic targets. Subsequently, a screening of approximately 9000 compounds from the FDA library against the identified drug target aims to delineate efficacious agents for combating S. flexneri serotype X infections. The application of subtractive genomics methodology yields prognostic insights, unveiling non-paralogous proteins (n = 4122), non-homologues (n = 1803), essential (n = 1246), drug-like (n = 389), resistant (n = 167), alongside 42 virulent proteins within the reference proteome. This iterative process culminates in the identification of Serine O-acetyltransferase as a viable drug target. Subsequent virtual screening endeavors to unearth FDA-approved medicinal compounds capable of inhibiting Serine O-acetyltransferase. Noteworthy candidates such as DB12983, DB15085, DB16098, DB16185, and DB16262 emerge, exhibiting potential for mitigating S. flexneri Serotype X. Despite the auspicious findings, diligent scrutiny is imperative to ascertain the efficacy and safety profile of the proposed drug candidates vis-à-vis S. flexneri.

Comprehensive bioinformatics-based annotation and functional characterization of bovine chymosin protein revealed novel biological insights

Chymosin, an aspartic protease present in the stomachs of young ruminants like cows (bovine), causes milk coagulation and cheese production through the breakdown of κ-casein peptide bonds at the Met105-Phe106 site. Bovine chymosin is first synthesized as a pre-prochymosin that is cleaved to produce the mature chymosin protein. Despite significant strides in research, our understanding of this crucial enzyme remains incomplete. The purpose of this work was to perform in silico evolutionary and functional analysis and to gain unique insights into the structure of this protein. For this, the sequence of Bos taurus chymosin from UniProt database was subjected to various bioinformatics analyses. We found that bovine chymosin is a low molecular weight and hydrophilic protein that has homologs in other Bovidae species. Two active sites of aspartic peptidases, along with a functional domain, were identified. Gene Ontology analysis further confirmed chymosin's involvement in proteolysis and aspartic endopeptidase activity. Potential disordered residues and post-translational modification sites were also uncovered. It was revealed that the secondary structure of bovine chymosin is comprised of beta strands (44.27%), coils (43.65%), and alpha helices (12.07%). A highly optimized 3D structure was also obtained. Moreover, crucial protein-protein interactions were unveiled. Altogether, these findings provide valuable insights that could guide future research on bovine chymosin and its biological roles.

Antitumoral activity of different Amaryllidaceae alkaloids: In vitro and in silico assays

ETHNOPHARMACOLOGY RELEVANCE

The plants of Amaryllidaceae family, such as Amaryllis belladonna L., have been used as herbal remedies for thousands of years to address various disorders, including diseases that might today be identified as cancer.

AIM OF THE STUDY

The objective of this work was to evaluate the potential of three Amaryllidaceae alkaloids against four cancer cell lines.

MATERIAL AND METHODS

The alkaloids lycorine, 1-O-acetylcaranine, and montanine were evaluated in vitro against colon adenocarcinoma cell line (HCT-116) and breast carcinoma cell lines (MCF-7, MDAMB231, and Hs578T). Computational experiments (target prediction and molecular docking) were conducted to gain a deeper comprehension of possible interactions between these alkaloids and potential targets associated with these tumor cells.

RESULTS

Montanine presented the best results against HCT-116, MDAMB231, and Hs578T cell lines, while lycorine was the most active against MCF-7. In alignment with the target prediction outcomes and existing literature, four potential targets were chosen for the molecular docking analysis: CDK8, EGFR, ER-alpha, and dCK. The docking scores revealed two potential targets for the alkaloids with scores similar to co-crystallized inhibitors and substrates: CDK8 and dCK. A visual analysis of the optimal docked configurations indicates that the alkaloids may interact with some key residues in contrast to the other docked compounds. This observation implies their potential to bind effectively to both targets.

CONCLUSIONS

In vitro and in silico results corroborate with data literature suggesting the Amaryllidaceae alkaloids as interesting molecules with antitumoral properties, especially montanine, which showed the best in vitro results against colorectal and breast carcinoma. More studies are necessary to confirm the targets and pharmaceutical potential of montanine against these cancer cell lines.

Cumulative phylogenetic, sequence and structural analysis of Insulin superfamily proteins provide unique structure-function insights

The insulin superfamily proteins (ISPs), in particular, insulin, IGFs and relaxin proteins are key modulators of animal physiology. They are known to have evolved from the same ancestral gene and have diverged into proteins with varied sequences and distinct functions, but maintain a similar structural architecture stabilized by highly conserved disulphide bridges. The recent surge of sequence data and the structures of these proteins prompted a need for a comprehensive analysis, which connects the evolution of these sequences (427 sequences) in the light of available functional and structural information including representative complex structures of ISPs with their cognate receptors. This study reveals (a) unusually high sequence conservation of IGFs (>90 % conservation in 184 sequences) and provides a possible structure-based rationale for such high sequence conservation; (b) provides an updated definition of the receptor-binding signature motif of the functionally diverse relaxin family members (c) provides a probable non-canonical C-peptide cleavage site in a few insulin sequences. The high conservation of IGFs appears to represent a classic case of resistance to sequence diversity exerted by physiologically important interactions with multiple partners. We also propose a probable mechanism for C-peptide cleavage in a few distinct insulin sequences and redefine the receptor-binding signature motif of the relaxin family. Lastly, we provide a basis for minimally modified insulin mutants with potential therapeutic application, inspired by concomitant changes observed in other insulin superfamily protein members supported by molecular dynamics simulation.

Yixin-Fumai granules modulate autophagy through the PI3K/AKT/FOXO pathway and lead to amelioration of aging mice with sick sinus syndrome

OBJECTIVE

By employing network pharmacology alongside molecular docking techniques, we can delve into the intricate workings of Yixin-Fumai granules (YXFMs) and their impact on sick sinus syndrome (SSS) within wrinkles mice. Specifically, we aim to understand how YXFMs enhance autophagy through the PI3K/AKT/FOXO path.

METHODS

The active ingredients and medicinal uses of Ginseng, ligusticum wallichii, Ophiopogon, Schisandra, salvia, and astragalus were compiled using the BATMAN-TCM database. We also used Genecards, OMIM, and Disgenet files to identify the disease goals. A hierarchical diagram of "disease-drug-key targets" was generated using the Cytoscape programs. In addition, we established a target protein interaction (PPI) network using the STRING database. Then, the Cluster Profiler R package was used to conduct GO functional enrichment evaluation and KEGG pathway enrichment analyses of the targets. Based on the PPI system, we chose the top communicating targets and substances over molecular docking. In vivo studies were performed to validate these selections further. The mouse model was induced to study the damaged sinoatrial node (SAN) in mice with lower heart rates due to age-related changes. Electrocardiogram and Masson staining assessments were performed to obtain the results. The transmission electron microscope was used to assess the autophagy level of SAN cells. Western blot was employed to analyze the impact of YXFMs on protein expression in the PI3K/AKT/FOXO signaling process throughout SSS therapy in aging mice.

RESULTS

One hundred forty-two active ingredients, 1858 targets, 1226 disease targets, and 266 intersection targets were obtained. The key targets of the PPI network encompassed TP53, AKT1, CTNNB1, INS, and TNF, among others. According to GO functional analysis, the mechanism underlying YXFMs in SSS treatment may primarily be associated with the control of ion transport across membranes, cardiac contraction, regulation of blood circulation, and other biological processes. Based on the results of KEGG pathway enrichment analysis, it was determined that they were mainly enriched in multiple pathways of signaling such as the PI3K-Akt signaling route, MAPK signaling process, AGE-RAGE signaling path, FOXO signaling path, HIF-1 signaling process, and several other paths. Molecular docking demonstrated that five compounds had excellent binding to the key candidate target proteins AKT1 and INS. Through the in vivo studies, we noticed notable effects when administering YXFMs. These effects included the suppression of aging-induced SSS, a decrease in the R-R interval, a rise in heart rate, a reduction in fibrosis, a boost in the autophagy process level, and a spike in the levels of expression of key protein molecules in the PI3K/AKT/FOXO signaling path.

CONCLUSION

This research has made preliminary predictions about the potential of YXFMs in treating SSS. It suggests that YXFMs may have the ability to target key proteins and critical paths associated with the condition. Further testing has been conducted to discover new findings and evidence of ideas for tackling SSS triggered by aging.

tracerDB: a crowdsourced fluorescent tracer database for target engagement analysis

Investigating ligand-protein complexes is essential in the areas of chemical biology and drug discovery. However, detailed information on key reagents such as fluorescent tracers and associated data for the development of widely used bioluminescence resonance energy transfer (BRET) assays including NanoBRET, time-resolved Förster resonance energy transfer (TR-FRET) and fluorescence polarization (FP) assays are not easily accessible to the research community. We created tracerDB, a curated database of validated tracers. This resource provides an open access knowledge base and a unified system for tracer and assay validation. The database is freely available at https://www.tracerdb.org/ .

CASTpFold: Computed Atlas of Surface Topography of the universe of protein Folds

Geometric and topological properties of protein structures, including surface pockets, interior cavities and cross channels, are of fundamental importance for proteins to carry out their functions. Computed Atlas of Surface Topography of proteins (CASTp) is a widely used web server for locating, delineating, and measuring these geometric and topological properties of protein structures. Recent developments in AI-based protein structure prediction such as AlphaFold2 (AF2) have significantly expanded our knowledge on protein structures. Here we present CASTpFold, a continuation of CASTp that provides accurate and comprehensive identifications and quantifications of protein topography. It now provides (i) results on an expanded database of proteins, including the Protein Data Bank (PDB) and non-singleton representative structures of AlphaFold2 structures, covering 183 million AF2 structures; (ii) functional pockets prediction with corresponding Gene Ontology (GO) terms or Enzyme Commission (EC) numbers for AF2-predicted structures and (iii) pocket similarity search function for surface and protein-protein interface pockets. The CASTpFold web server is freely accessible at https://cfold.bme.uic.edu/castpfold/.

GPS-SUMO 2.0: an updated online service for the prediction of SUMOylation sites and SUMO-interacting motifs

Small ubiquitin-like modifiers (SUMOs) are tiny but important protein regulators involved in orchestrating a broad spectrum of biological processes, either by covalently modifying protein substrates or by noncovalently interacting with other proteins. Here, we report an updated server, GPS-SUMO 2.0, for the prediction of SUMOylation sites and SUMO-interacting motifs (SIMs). For predictor training, we adopted three machine learning algorithms, penalized logistic regression (PLR), a deep neural network (DNN), and a transformer, and used 52 404 nonredundant SUMOylation sites in 8262 proteins and 163 SIMs in 102 proteins. To further increase the accuracy of predicting SUMOylation sites, a pretraining model was first constructed using 145 545 protein lysine modification sites, followed by transfer learning to fine-tune the model. GPS-SUMO 2.0 exhibited greater accuracy in predicting SUMOylation sites than did other existing tools. For users, one or multiple protein sequences or identifiers can be input, and the prediction results are shown in a tabular list. In addition to the basic statistics, we integrated knowledge from 35 public resources to annotate SUMOylation sites or SIMs. The GPS-SUMO 2.0 server is freely available at https://sumo.biocuckoo.cn/. We believe that GPS-SUMO 2.0 can serve as a useful tool for further analysis of SUMOylation and SUMO interactions.

Toxic effects of fragmented polyethylene terephthalate particles on the marine rotifer Brachionus koreanus: Based on ingestion and egestion assay, in vivo toxicity test, and multi-omics analysis

Microplastics (MPs) are a major concern in marine ecosystem because MPs are persistent and ubiquitous in oceans and are easily consumed by marine biota. Although many studies have reported the toxicity of MPs to marine biota, the toxicity of environmentally relevant types of MPs is little understood. We investigated the toxic effects of fragmented polyethylene terephthalate (PET) MP, one of the most abundant MPs in the ocean, on the marine rotifer Brachionus koreanus at the individual and molecular level. No significant rotifer mortality was observed after exposure to PET MPs for 24 and 48 h. The ingestion and egestion assays showed that rotifers readily ingested PET MPs in the absence of food but not when food was supplied; thus, there were also no chronic effects of PET MPs. In contrast, intracellular reactive oxygen species levels and glutathione S-transferase activity in rotifers were significantly increased by PET MPs. Transcriptomic and metabolomic analyses revealed that genes and metabolites related to energy metabolism and immune processes were significantly affected by PET MPs in a concentration-dependent manner. Although acute toxicity of PET MPs was not observed, PET MPs are potentially toxic to the antioxidant system, immune system, and energy metabolism in rotifers.

Human gut microbiota analysis of cystic fibrosis infants using metaproteomics

We report a metaproteomic analysis of the gut microbiota of eight infants with cystic fibrosis, during the first year of life. This is the first study in this disease that uses metaproteomics to analyze stool samples from patients at such a young age.

Transformers meets neoantigen detection: a systematic literature review

Cancer immunology offers a new alternative to traditional cancer treatments, such as radiotherapy and chemotherapy. One notable alternative is the development of personalized vaccines based on cancer neoantigens. Moreover, Transformers are considered a revolutionary development in artificial intelligence with a significant impact on natural language processing (NLP) tasks and have been utilized in proteomics studies in recent years. In this context, we conducted a systematic literature review to investigate how Transformers are applied in each stage of the neoantigen detection process. Additionally, we mapped current pipelines and examined the results of clinical trials involving cancer vaccines.

Mechanism Analysis of Selenium-Containing Compounds in Alleviating Spinal Cord Injury Based on Network Pharmacology and Molecular Docking Technology

Spinal cord injury (SCI) is a severe traumatic condition in spinal surgery characterized by nerve damage in and below the injured area. Despite advancements in understanding the pathophysiology of SCI, effective clinical treatments remain elusive. Selenium compounds have become a research hotspot due to their diverse medicinal activities. Previously, our group synthesized a selenium-containing Compound 34# with significant anti-inflammatory activity. This study aimed to explore the anti-SCI effects of selenium-containing compounds using network pharmacology, molecular docking (MD), and ADMET methods. To identify SCI-related targets and those associated with 34#, GeneCards, NCBI, and SEA databases were employed. Eight overlapping targets were considered candidate targets, and molecular docking was performed using the PDB database and AutoDock software. The STRING database was used to obtain protein-protein interactions (PPI). Molecular dynamics simulation, MM/GBSA binding free energy score, and ADMET prediction were used to evaluate the potential targets and drug properties of 34#. Finally, experiments on NSC34 cells and mice were to verify the effects of 34# on SCI. Our results revealed eight candidate targets for 34# in the treatment of SCI. PPI and MD identified ADRB2 and HTR1F as the highest connectivity with 34#. ADMET analysis confirmed the low toxicity and safety of 34#. In vitro and in vivo models validated the anti-SCI effects. Our study elucidated candidate targets for alleviating SCI with 34#, explored PPI and target-related signaling pathways, and validated its anti-SCI effects. These findings enhance our understanding of 34#'s mechanism in treating SCI, positioning it as a potential candidate for SCI prevention.

Characterization of thiamine pyrophosphokinase of vitamin B1 biosynthetic pathway as a drug target of Leishmania donovani

Vitamin B1 is an essential cofactor for enzymes involved in the metabolism of carbohydrates, particularly Transketolases. These enzymes are amenable to therapeutic interventions because of their specificity. In the final step of the Vitamin B1 biosynthesis pathway, Thiamine Pyrophosphokinase (TPK) converts thiamin into its active form, Thiamin Pyrophosphate (TPP), allowing researchers to investigate the functional importance of this enzyme and the pathway's dispensability in Leishmania donovani, a protozoan parasite that causes visceral leishmaniasis. In this study, various in silico, biochemical, biophysical, and cellular assays-based experiments have been conducted to identify and characterize LdTPK, and to provide a sound platform for the discovery of potential LdTPK inhibitors. LdTPK structural modelling ensured high protein quality. Oxythiamine and pyrithiamine were found to bind well with LdTPK with considerable binding energies, and MD simulation-based experiments indicated the stability of the complexation. Additionally, LdTPK1 was found to activate ROS defense in amastigotes, and its inhibition using oxythiamine and pyrithiamine led to the growth inhibition of L. donovani promastigotes and intracellular amastigotes. These findings highlight LdTPK as a promising target for the development of new anti-leishmanial agents. An in-depth analysis of the enzymes involved in TPP biosynthesis in L. donovani has the potential to yield novel therapeutic strategies for Leishmaniasis.Communicated by Ramaswamy H. Sarma.

Comprehensive analysis of metal(loid)s and associated metal(loid) resistance genes in atmospheric particulate matter

Despite global concerns about metal(loid)s in atmospheric particulate matter (PM), the presence of metal(loid) resistance genes (MRGs) in PM remains unknown. Therefore, we conducted a comprehensive investigation of the metal(loid)s and associated MRGs in PMs in two seasons (summer and winter) in Xiamen, China. According to the geoaccumulation index (Igeo), most metal(loid)s, except for V and Mn, exhibited enrichment in PM, suggesting potential anthropogenic sources. By employing Positive Matrix Factorization (PMF) model, utilizing a dataset encompassing both total and bioaccessible metal(loid)s, along with backward trajectory simulations, traffic emissions were determined to be the primary potential contributor of metal(loid)s in summer, whereas coal combustion was observed to have a dominant contribution in winter. The major contributor to the carcinogenic risk of metal(loid)s in both summer and winter was predominantly attributed to coal combustion, which serves as the main source of bioaccessible Cr. Bacterial communities within PMs showed lower diversity and network complexity in summer than in winter, with Pseudomonadales being the dominant order. Abundant MRGs, including the As(III) S-adenosylmethionine methyltransferase gene (arsM), Cu(I)-translocating P-type ATPase gene (copA), Zn(II)/Cd(II)/Pb(II)-translocating P-type ATPase gene (zntA), and Zn(II)-translocating P-type ATPase gene (ziaA), were detected within the PMs. Seasonal variations were observed for the metal(loid) concentration, bacterial community structure, and MRG abundance. The bacterial community composition and MRG abundance within PMs were primarily influenced by temperature, rather than metal(loid)s. This research offers novel perspectives on the occurrence of metal(loid)s and MRGs in PMs, thereby contributing to the control of air pollution.

KbhbXG: A Machine learning architecture based on XGBoost for prediction of lysine β-Hydroxybutyrylation (Kbhb) modification sites

Lysine β-hydroxybutyrylation is an important post-translational modification (PTM) involved in various physiological and biological processes. In this research, we introduce a novel predictor KbhbXG, which utilizes XGBoost to identify β-hydroxybutyrylation modification sites based on protein sequence information. The traditional experimental methods employed for the identification of β-hydroxybutyrylated sites using proteomic techniques are both costly and time-consuming. Thus, the development of computational methods and predictors can play a crucial role in facilitating the rapid identification of β-hydroxybutyrylation sites. Our proposed KbhbXG model first utilizes machine learning algorithm XGBoost to predict β-hydroxybutyrylation modification sites. On the independent test set, KbhbXG achieves an accuracy of 0.7457, specificity of 0.7771, and an impressive area under the curve (AUC) score of 0.8172. The high AUC score achieved by our method demonstrates its potential for effectively identifying novel β-hydroxybutyrylation sites, thereby facilitating further research and exploration of the β-hydroxybutyrylation process. Also, functional analyses have revealed that different organisms preferentially engage in distinct biological processes and pathways, which can provide valuable insights for understanding the mechanism of β-hydroxybutyrylation and guide experimental verification. To promote transparency and reproducibility, we have made both the codes and dataset of KbhbXG publicly available. Researchers interested in utilizing our proposed model can access these resources at https://github.com/Lab-Xu/KbhbXG.

Structural bases for stoichiometry-selective calcium potentiation of a neuronal nicotinic receptor

BACKGROUND AND PURPOSE

α4β2 nicotinic acetylcholine (nACh) receptors assemble in two stoichiometric forms, one of which is potentiated by calcium. The sites of calcium binding that underpin potentiation are not known.

EXPERIMENTAL APPROACH

To identify calcium binding sites, we applied cryo-electron microscopy (cryo-EM) and molecular dynamics (MD) simulations to each stoichiometric form of the α4β2 nACh receptor in the presence of calcium ions. To test whether the identified calcium sites are linked to potentiation, we generated mutants of anionic residues at the sites, expressed wild type and mutant receptors in clonal mammalian fibroblasts, and recorded ACh-elicited single-channel currents with or without calcium.

KEY RESULTS

Both cryo-EM and MD simulations show calcium bound to a site between the extracellular and transmembrane domains of each α4 subunit (ECD-TMD site). Substituting alanine for anionic residues at the ECD-TMD site abolishes stoichiometry-selective calcium potentiation, as monitored by single-channel patch clamp electrophysiology. Additionally, MD simulation reveals calcium association at subunit interfaces within the extracellular domain. Substituting alanine for anionic residues at the ECD sites reduces or abolishes stoichiometry-selective calcium potentiation.

CONCLUSIONS AND IMPLICATIONS

Stoichiometry-selective calcium potentiation of the α4β2 nACh receptor is achieved by calcium association with topographically distinct sites framed by anionic residues within the α4 subunit and between the α4 and β2 subunits. Stoichiometry-selective calcium potentiation could result from the greater number of calcium sites in the stoichiometric form with three rather than two α4 subunits. The results are relevant to modulation of signalling via α4β2 nACh receptors in physiological and pathophysiological conditions.

CKG-IMC: An inductive matrix completion method enhanced by CKG and GNN for Alzheimer's disease compound-protein interactions prediction

Alzheimer's disease (AD) is one of the most prevalent chronic neurodegenerative disorders globally, with a rapidly growing population of AD patients and currently no effective therapeutic interventions available. Consequently, the development of therapeutic anti-AD drugs and the identification of AD targets represent one of the most urgent tasks. In this study, in addition to considering known drugs and targets, we explore compound-protein interactions (CPIs) between compounds and proteins relevant to AD. We propose a deep learning model called CKG-IMC to predict Alzheimer's disease compound-protein interaction relationships. CKG-IMC comprises three modules: a collaborative knowledge graph (CKG), a principal neighborhood aggregation graph neural network (PNA), and an inductive matrix completion (IMC). The collaborative knowledge graph is used to learn semantic associations between entities, PNA is employed to extract structural features of the relationship network, and IMC is utilized for CPIs prediction. Compared with a total of 16 baseline models based on similarities, knowledge graphs, and graph neural networks, our model achieves state-of-the-art performance in experiments of 10-fold cross-validation and independent test. Furthermore, we use CKG-IMC to predict compounds interacting with two confirmed AD targets, 42-amino-acid β-amyloid (Aβ42) protein and microtubule-associated protein tau (tau protein), as well as proteins interacting with five FDA-approved anti-AD drugs. The results indicate that the majority of predictions are supported by literature, and molecular docking experiments demonstrate a strong affinity between the predicted compounds and targets.

Self-assembling viral histones are evolutionary intermediates between archaeal and eukaryotic nucleosomes

Nucleosomes are DNA-protein complexes composed of histone proteins that form the basis of eukaryotic chromatin. The nucleosome was a key innovation during eukaryotic evolution, but its origin from histone homologues in Archaea remains unclear. Viral histone repeats, consisting of multiple histone paralogues within a single protein, may reflect an intermediate state. Here we examine the diversity of histones encoded by Nucleocytoviricota viruses. We identified 258 histones from 168 viral metagenomes with variable domain configurations including histone singlets, doublets, triplets and quadruplets, the latter comprising the four core histones arranged in series. Viral histone repeats branch phylogenetically between Archaea and eukaryotes and display intermediate functions in Escherichia coli, self-assembling into eukaryotic-like nucleosomes that stack into archaeal-like oligomers capable of impacting genomic activity and condensing DNA. Histone linkage also facilitates nucleosome formation, promoting eukaryotic histone assembly in E. coli. These data support the hypothesis that viral histone repeats originated in stem-eukaryotes and that nucleosome evolution proceeded through histone repeat intermediates.

DRTerHGAT: A drug repurposing method based on the ternary heterogeneous graph attention network

Drug repurposing is an effective method to reduce the time and cost of drug development. Computational drug repurposing can quickly screen out the most likely associations from large biological databases to achieve effective drug repurposing. However, building a comprehensive model that integrates drugs, proteins, and diseases for drug repurposing remains challenging. This study proposes a drug repurposing method based on the ternary heterogeneous graph attention network (DRTerHGAT). DRTerHGAT designs a novel protein feature extraction process consisting of a large-scale protein language model and a multi-task autoencoder, so that protein features can be extracted accurately and efficiently from amino acid sequences. The ternary heterogeneous graph of drug-protein-disease comprehensively considering the relationships among the three types of nodes, including three homogeneous and three heterogeneous relationships. Based on the graph and the extracted protein features, the deep features of the drugs and the diseases are extracted by graph convolutional networks (GCN) and heterogeneous graph node attention networks (HGNA). In the experiments, DRTerHGAT is proven superior to existing advanced methods and DRTerHGAT variants. DRTerHGAT's powerful ability for drug repurposing is also demonstrated in Alzheimer's disease.

The genomics of mimicry: Gene expression throughout development provides insights into convergent and divergent phenotypes in a Müllerian mimicry system

A common goal in evolutionary biology is to discern the mechanisms that produce the astounding diversity of morphologies seen across the tree of life. Aposematic species, those with a conspicuous phenotype coupled with some form of defence, are excellent models to understand the link between vivid colour pattern variations, the natural selection shaping it, and the underlying genetic mechanisms underpinning this variation. Mimicry systems in which species share a conspicuous phenotype can provide an even better model for understanding the mechanisms of colour production in aposematic species, especially if comimics have divergent evolutionary histories. Here we investigate the genetic mechanisms by which mimicry is produced in poison frogs. We assembled a 6.02-Gbp genome with a contig N50 of 310 Kbp, a scaffold N50 of 390 Kbp and 85% of expected tetrapod genes. We leveraged this genome to conduct gene expression analyses throughout development of four colour morphs of Ranitomeya imitator and two colour morphs from both R. fantastica and R. variabilis which R. imitator mimics. We identified a large number of pigmentation and patterning genes differentially expressed throughout development, many of them related to melanophores/melanin, iridophore development and guanine synthesis. We also identify the pteridine synthesis pathway (including genes such as qdpr and xdh) as a key driver of the variation in colour between morphs of these species, and identify several plausible candidates for colouration in vertebrates (e.g. cd36, ep-cadherin and perlwapin). Finally, we hypothesise that keratin genes (e.g. krt8) are important for producing different structural colours within these frogs.

Metagenomic-based discovery and comparison of the lignin degrading potential of microbiomes in aquatic and terrestrial ecosystems via the LCdb database

Lignin, as an abundant organic carbon, plays a vital role in the global carbon cycle. However, our understanding of the global lignin-degrading microbiome remains elusive. The greatest barrier has been absence of a comprehensive and accurate functional gene database. Here, we first developed a curated functional gene database (LCdb) for metagenomic profiling of lignin degrading microbial consortia. Via the LCdb, we draw a clear picture describing the global biogeography of communities with lignin-degrading potential. They exhibit clear niche differentiation at the levels of taxonomy and functional traits. The terrestrial microbiomes showed the highest diversity, yet the lowest correlations. In particular, there were few correlations between genes involved in aerobic and anaerobic degradation pathways, showing a clear functional redundancy property. In contrast, enhanced correlations, especially closer inter-connections between anaerobic and aerobic groups, were observed in aquatic consortia in response to the lower diversity. Specifically, dypB and dypA, are widespread on Earth, indicating their essential roles in lignin depolymerization. Estuarine and marine consortia featured the laccase and mnsod genes, respectively. Notably, the roles of archaea in lignin degradation were revealed in marine ecosystems. Environmental factors strongly influenced functional traits, but weakly shaped taxonomic groups. Null mode analysis further verified that composition of functional traits was deterministic, while taxonomic composition was highly stochastic, demonstrating that the environment selects functional genes rather than taxonomic groups. Our study not only develops a useful tool to study lignin degrading microbial communities via metagenome sequencing but also advances our understanding of ecological traits of these global microbiomes.

In-silico study of protein-protein interactions in wheat blast using docking and molecular dynamics simulation approach

Despite advancements in agricultural research and the introduction of modern biotechnological and farming techniques, food security remains a significant issue. Although the efforts of farmers to meet the demands of a growing population, many plant diseases caused by pathogens, through their effects on cell division and tissue growth, lead to the annual loss of countless food crops. The recently emerged wheat blast fungus Magnaporthe oryzae pathotype Triticum (MoT) poses a significant danger to worldwide wheat cultivation. The fungus is a highly varied lineage of the M. oryzae, responsible for causing rice blast disease. In spite of being a significant challenge to successful wheat production in South America since 1985, the underlying biology of the wheat blast pathogen is still not fully understood. The initial outbreak of the wheat blast in South Asia had a severe impact on wheat production, resulting in a complete loss of yield in affected fields. For the purpose of enhancing disease management, it's vital to acquire a comprehensive comprehension of the infection biology of the fungus and its interaction with wheat plants on molecular levels. Host-pathogen protein interactions (HPIs) have the potential to reveal the pathogens' mechanism for overcoming the host organism. The current study delves into the interactions between the host plant wheat and MoT through protein-protein interactions, molecular docking, and 100 ns molecular dynamic simulations. This research uncovers the structural and functional basis of these proteins, leading to improved plant health and production.Communicated by Ramaswamy H. Sarma.

ISTransbase: an online database for inhibitor and substrate of drug transporters

Drug transporters, integral membrane proteins found throughout the human body, play critical roles in physiological and biochemical processes through interactions with ligands, such as substrates and inhibitors. The extensive and disparate data on drug transporters complicate understanding their complex relationships with ligands. To address this challenge, it is essential to gather and summarize information on drug transporters, inhibitors and substrates, and simultaneously develop a comprehensive and user-friendly database. Current online resources often provide fragmented information and have limited coverage of drug transporter substrates and inhibitors, highlighting the need for a specialized, comprehensive and openly accessible database. ISTransbase addresses this gap by amassing a substantial amount of data from literature, government documents and open databases. It includes 16 528 inhibitors and 4465 substrates of 163 drug transporters from 18 different species, resulting in a total of 93 841 inhibitor records and 51 053 substrate records. ISTransbase provides detailed insights into drug transporters and their inhibitors/substrates, encompassing transporter and molecule structure, transporter function and distribution, as well as experimental methods and results from transport or inhibition experiments. Furthermore, ISTransbase offers three search strategies that allow users to retrieve drugs and transporters based on multiple selectable constraints, as well as perform checks for drug-drug interactions. Users can also browse and download data. In summary, ISTransbase (https://istransbase.scbdd.com/) serves as a valuable resource for accurately and efficiently accessing information on drug transporter inhibitors and substrates, aiding researchers in exploring drug transporter mechanisms and assisting clinicians in mitigating adverse drug reactions Database URL: https://istransbase.scbdd.com/.

Insights into the structural and functional activities of forgotten Kinases: PCTAIREs CDKs

In cells, signal transduction heavily relies on the intricate regulation of protein kinases, which provide the fundamental framework for modulating most signaling pathways. Dysregulation of kinase activity has been implicated in numerous pathological conditions, particularly in cancer. The druggable nature of most kinases positions them into a focal point during the process of drug development. However, a significant challenge persists, as the role and biological function of nearly one third of human kinases remains largely unknown.Within this diverse landscape, cyclin-dependent kinases (CDKs) emerge as an intriguing molecular subgroup. In human, this kinase family encompasses 21 members, involved in several key biological processes. Remarkably, 13 of these CDKs belong to the category of understudied kinases, and only 5 having undergone broad investigation to date. This knowledge gap underscores the pressing need to delve into the study of these kinases, starting with a comprehensive review of the less-explored ones.Here, we will focus on the PCTAIRE subfamily of CDKs, which includes CDK16, CDK17, and CDK18, arguably among the most understudied CDKs members. To contextualize PCTAIREs within the spectrum of human pathophysiology, we conducted an exhaustive review of the existing literature and examined available databases. This approach resulted in an articulate depiction of these PCTAIREs, encompassing their expression patterns, 3D configurations, mechanisms of activation, and potential functions in normal tissues and in cancer.We propose that this effort offers the possibility of identifying promising areas of future research that extend from basic research to potential clinical and therapeutic applications.

Probabilistic pathway-based multimodal factor analysis

MOTIVATION

Multimodal profiling strategies promise to produce more informative insights into biomedical cohorts via the integration of the information each modality contributes. To perform this integration, however, the development of novel analytical strategies is needed. Multimodal profiling strategies often come at the expense of lower sample numbers, which can challenge methods to uncover shared signals across a cohort. Thus, factor analysis approaches are commonly used for the analysis of high-dimensional data in molecular biology, however, they typically do not yield representations that are directly interpretable, whereas many research questions often center around the analysis of pathways associated with specific observations.

RESULTS

We develop PathFA, a novel approach for multimodal factor analysis over the space of pathways. PathFA produces integrative and interpretable views across multimodal profiling technologies, which allow for the derivation of concrete hypotheses. PathFA combines a pathway-learning approach with integrative multimodal capability under a Bayesian procedure that is efficient, hyper-parameter free, and able to automatically infer observation noise from the data. We demonstrate strong performance on small sample sizes within our simulation framework and on matched proteomics and transcriptomics profiles from real tumor samples taken from the Swiss Tumor Profiler consortium. On a subcohort of melanoma patients, PathFA recovers pathway activity that has been independently associated with poor outcome. We further demonstrate the ability of this approach to identify pathways associated with the presence of specific cell-types as well as tumor heterogeneity. Our results show that we capture known biology, making it well suited for analyzing multimodal sample cohorts.

AVAILABILITY AND IMPLEMENTATION

The tool is implemented in python and available at https://github.com/ratschlab/path-fa.

Extremely halophilic brine community manipulation shows higher robustness of microbiomes inhabiting human-driven solar saltern than naturally driven lake

Hypersaline ecosystems display taxonomically similar assemblages with low diversities and highly dense accompanying viromes. The ecological implications of viral infection on natural microbial populations remain poorly understood, especially at finer scales of diversity. Here, we sought to investigate the influence of changes in environmental physicochemical conditions and viral predation pressure by autochthonous and allochthonous viruses on host dynamics. For this purpose, we transplanted two microbiomes coming from distant hypersaline systems (solar salterns of Es Trenc in Spain and the thalassohaline lake of Aran-Bidgol lake in Iran), by exchanging the cellular fractions with the sterile-filtered accompanying brines with and without the free extracellular virus fraction. The midterm exposure (1 month) of the microbiomes to the new conditions showed that at the supraspecific taxonomic range, the assemblies from the solar saltern brine more strongly resisted the environmental changes and viral predation than that of the lake. The metagenome-assembled genomes (MAGs) analysis revealed an intraspecific transition at the ecotype level, mainly driven by changes in viral predation pressure, by both autochthonous and allochthonous viruses.

IMPORTANCE

Viruses greatly influence succession and diversification of their hosts, yet the effects of viral infection on the ecological dynamics of natural microbial populations remain poorly understood, especially at finer scales of diversity. By manipulating the viral predation pressure by autochthonous and allochthonous viruses, we uncovered potential phage-host interaction, and their important role in structuring the prokaryote community at an ecotype level.

Alternative proteoforms and proteoform-dependent assemblies in humans and plants

The variability of proteins at the sequence level creates an enormous potential for proteome complexity. Exploring the depths and limits of this complexity is an ongoing goal in biology. Here, we systematically survey human and plant high-throughput bottom-up native proteomics data for protein truncation variants, where substantial regions of the full-length protein are missing from an observed protein product. In humans, Arabidopsis, and the green alga Chlamydomonas, approximately one percent of observed proteins show a short form, which we can assign by comparison to RNA isoforms as either likely deriving from transcript-directed processes or limited proteolysis. While some detected protein fragments align with known splice forms and protein cleavage events, multiple examples are previously undescribed, such as our observation of fibrocystin proteolysis and nuclear translocation in a green alga. We find that truncations occur almost entirely between structured protein domains, even when short forms are derived from transcript variants. Intriguingly, multiple endogenous protein truncations of phase-separating translational proteins resemble cleaved proteoforms produced by enteroviruses during infection. Some truncated proteins are also observed in both humans and plants, suggesting that they date to the last eukaryotic common ancestor. Finally, we describe novel proteoform-specific protein complexes, where the loss of a domain may accompany complex formation.

Advancing microbial production through artificial intelligence-aided biology

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.

Genomic insights into the evolution of secondary metabolism of Escovopsis and its allies, specialized fungal symbionts of fungus-farming ants

The metabolic intimacy of symbiosis often demands the work of specialists. Natural products and defensive secondary metabolites can drive specificity by ensuring infection and propagation across host generations. But in contrast to bacteria, little is known about the diversity and distribution of natural product biosynthetic pathways among fungi and how they evolve to facilitate symbiosis and adaptation to their host environment. In this study, we define the secondary metabolism of Escovopsis and closely related genera, symbionts in the gardens of fungus-farming ants. We ask how the gain and loss of various biosynthetic pathways correspond to divergent lifestyles. Long-read sequencing allowed us to define the chromosomal features of representative Escovopsis strains, revealing highly reduced genomes composed of seven to eight chromosomes. The genomes are highly syntenic with macrosynteny decreasing with increasing phylogenetic distance, while maintaining a high degree of mesosynteny. An ancestral state reconstruction analysis of biosynthetic pathways revealed that, while many secondary metabolites are shared with non-ant-associated Sordariomycetes, 56 pathways are unique to the symbiotic genera. Reflecting adaptation to diverging ant agricultural systems, we observe that the stepwise acquisition of these pathways mirrors the ecological radiations of attine ants and the dynamic recruitment and replacement of their fungal cultivars. As different clades encode characteristic combinations of biosynthetic gene clusters, these delineating profiles provide important insights into the possible mechanisms underlying specificity between these symbionts and their fungal hosts. Collectively, our findings shed light on the evolutionary dynamic nature of secondary metabolism in Escovopsis and its allies, reflecting adaptation of the symbionts to an ancient agricultural system.IMPORTANCEMicrobial symbionts interact with their hosts and competitors through a remarkable array of secondary metabolites and natural products. Here, we highlight the highly streamlined genomic features of attine-associated fungal symbionts. The genomes of Escovopsis species, as well as species from other symbiont genera, many of which are common with the gardens of fungus-growing ants, are defined by seven chromosomes. Despite a high degree of metabolic conservation, we observe some variation in the symbionts' potential to produce secondary metabolites. As the phylogenetic distribution of the encoding biosynthetic gene clusters coincides with attine transitions in agricultural systems, we highlight the likely role of these metabolites in mediating adaptation by a group of highly specialized symbionts.

Screening of bioactive compounds from selected mushroom species against putative drug targets in Mycobacterium tuberculosis: a multi-target approach

Mycobacterium tuberculosis (Mtb) is a notorious pathogen that causes one of the highest mortalities globally. Due to a pressing demand to identify novel therapeutic alternatives, the present study aims to focus on screening the putative drug targets and prioritizing their role in antibacterial drug development. The most vital proteins involved in the Biotin biosynthesis pathway and the Lipoarabinomannan (LAM) pathway such as biotin synthase (bioB) and alpha-(1->6)-mannopyranosyltransferase A (mptA) respectively, along with other essential virulence proteins of Mtb were selected as drug targets. Among these, the ones without native structures were modelled and validated using standard bioinformatics tools. Further, the interactions were performed with naturally available lead molecules present in selected mushroom species such as Agaricus bisporus, Pleurotus djamor, Hypsizygus ulmarius. Through Gas Chromatography-Mass Spectrometry (GC-MS), 15 bioactive compounds from the methanolic extract of mushrooms were identified. Further, 4 were selected based on drug-likeness and pharmacokinetic screening for molecular docking analysis against our prioritized targets wherein Benz[e]azulene from Pleurotus djamor illustrated a good binding affinity with a LF rank score of -9.036 kcal mol -1 against nuoM (NADH quinone oxidoreductase subunit M) and could be used as a prospective candidate in order to combat Tuberculosis (TB). Furthermore, the stability of the complex are validated using MD Simulations and subsequently, the binding free energy was calculated using MM-GBSA analysis. Thus, the current in silico analysis suggests a promising role of compounds extracted from mushrooms in tackling the TB burden.Communicated by Ramaswamy H. Sarma.

Reduced Retinal Pigment Epithelial Autophagy Due to Loss of Rab12 Prenylation in a Human iPSC-RPE Model of Choroideremia

Choroideremia is an X-linked chorioretinal dystrophy caused by mutations in CHM, encoding Rab escort protein 1 (REP-1), leading to under-prenylation of Rab GTPases (Rabs). Despite ubiquitous expression of CHM, the phenotype is limited to degeneration of the retina, retinal pigment epithelium (RPE), and choroid, with evidence for primary pathology in RPE cells. However, the spectrum of under-prenylated Rabs in RPE cells and how they contribute to RPE dysfunction remain unknown. A CRISPR/Cas-9-edited CHM-/- iPSC-RPE model was generated with isogenic control cells. Unprenylated Rabs were biotinylated in vitro and identified by tandem mass tag (TMT) spectrometry. Rab12 was one of the least prenylated and has an established role in suppressing mTORC1 signaling and promoting autophagy. CHM-/- iPSC-RPE cells demonstrated increased mTORC1 signaling and reduced autophagic flux, consistent with Rab12 dysfunction. Autophagic flux was rescued in CHM-/- cells by transduction with gene replacement (ShH10-CMV-CHM) and was reduced in control cells by siRNA knockdown of Rab12. This study supports Rab12 under-prenylation as an important cause of RPE cell dysfunction in choroideremia and highlights increased mTORC1 and reduced autophagy as potential disease pathways for further investigation.

From genome to evolution: investigating type II methylotrophs using a pangenomic analysis

A comprehensive pangenomic approach was employed to analyze the genomes of 75 type II methylotrophs spanning various genera. Our investigation revealed 256 exact core gene families shared by all 75 organisms, emphasizing their crucial role in the survival and adaptability of these organisms. Additionally, we predicted the functionality of 12 hypothetical proteins. The analysis unveiled a diverse array of genes associated with key metabolic pathways, including methane, serine, glyoxylate, and ethylmalonyl-CoA (EMC) metabolic pathways. While all selected organisms possessed essential genes for the serine pathway, Methylooceanibacter marginalis lacked serine hydroxymethyltransferase (SHMT), and Methylobacterium variabile exhibited both isozymes of SHMT, suggesting its potential to utilize a broader range of carbon sources. Notably, Methylobrevis sp. displayed a unique serine-glyoxylate transaminase isozyme not found in other organisms. Only nine organisms featured anaplerotic enzymes (isocitrate lyase and malate synthase) for the glyoxylate pathway, with the rest following the EMC pathway. Methylovirgula sp. 4MZ18 stood out by acquiring genes from both glyoxylate and EMC pathways, and Methylocapsa sp. S129 featured an A-form malate synthase, unlike the G-form found in the remaining organisms. Our findings also revealed distinct phylogenetic relationships and clustering patterns among type II methylotrophs, leading to the proposal of a separate genus for Methylovirgula sp. 4M-Z18 and Methylocapsa sp. S129. This pangenomic study unveils remarkable metabolic diversity, unique gene characteristics, and distinct clustering patterns of type II methylotrophs, providing valuable insights for future carbon sequestration and biotechnological applications.

IMPORTANCE

Methylotrophs have played a significant role in methane-based product production for many years. However, a comprehensive investigation into the diverse genetic architectures across different genera of methylotrophs has been lacking. This study fills this knowledge gap by enhancing our understanding of core hypothetical proteins and unique enzymes involved in methane oxidation, serine, glyoxylate, and ethylmalonyl-CoA pathways. These findings provide a valuable reference for researchers working with other methylotrophic species. Furthermore, this study not only unveils distinctive gene characteristics and phylogenetic relationships but also suggests a reclassification for Methylovirgula sp. 4M-Z18 and Methylocapsa sp. S129 into separate genera due to their unique attributes within their respective genus. Leveraging the synergies among various methylotrophic organisms, the scientific community can potentially optimize metabolite production, increasing the yield of desired end products and overall productivity.

Use of dairy industry side-stream lactose for tablet manufacturing - proof of concept study

During recent years there have been shortages of certain drugs due to problems in raw material supply. These are often related to active ingredients but could also affect excipients. Lactose is one of the most used excipients in tableting and comes in two anomeric and several solid-state forms. The aim of this study was to utilize lactose from a dairy side-stream and compare it against a commercial reference in direct compression. This would be a sustainable option and would secure domestic availability during crises. Two types of lactose, spray-dried and freeze-dried, were evaluated. Lactose was mixed with microcrystalline cellulose in different ratios together with lubricant and glidant, and flowability and tabletability of the formulations was characterized. The fully amorphous and small particle-sized spray-dried lactose flowed inadequately but exhibited good tabletability. The larger particle-sized, freeze-dried lactose exhibited sufficient flow and better tabletability than the commercial reference. However, disintegration and drug release were slower when using the investigational lactose formulations. This was most likely due to remaining milk proteins, especially caseins, in the lactose. Overall, the investigational lactose provides promise for the use of such a side-stream product during crisis situations but enhancing their properties and/or purity would be needed.

Current status and emerging frontiers in enzyme engineering: An industrial perspective

Protein engineering mechanisms can be an efficient approach to enhance the biochemical properties of various biocatalysts. Immobilization of biocatalysts and the introduction of new-to-nature chemical reactivities are also possible through the same mechanism. Discovering new protocols that enhance the catalytic active protein that possesses novelty in terms of being stable, active, and, stereoselectivity with functions could be identified as essential areas in terms of concurrent bioorganic chemistry (synergistic relationship between organic chemistry and biochemistry in the context of enzyme engineering). However, with our current level of knowledge about protein folding and its correlation with protein conformation and activities, it is almost impossible to design proteins with specific biological and physical properties. Hence, contemporary protein engineering typically involves reprogramming existing enzymes by mutagenesis to generate new phenotypes with desired properties. These processes ensure that limitations of naturally occurring enzymes are not encountered. For example, researchers have engineered cellulases and hemicellulases to withstand harsh conditions encountered during biomass pretreatment, such as high temperatures and acidic environments. By enhancing the activity and robustness of these enzymes, biofuel production becomes more economically viable and environmentally sustainable. Recent trends in enzyme engineering have enabled the development of tailored biocatalysts for pharmaceutical applications. For instance, researchers have engineered enzymes such as cytochrome P450s and amine oxidases to catalyze challenging reactions involved in drug synthesis. In addition to conventional methods, there has been an increasing application of machine learning techniques to identify patterns in data. These patterns are then used to predict protein structures, enhance enzyme solubility, stability, and function, forecast substrate specificity, and assist in rational protein design. In this review, we discussed recent trends in enzyme engineering to optimize the biochemical properties of various biocatalysts. Using examples relevant to biotechnology in engineering enzymes, we try to expatiate the significance of enzyme engineering with how these methods could be applied to optimize the biochemical properties of a naturally occurring enzyme.

Network pharmacology and untargeted metabolomic-based investigation of anti-osteoporotic effects of viscozyme-assisted polysaccharide from Portulaca oleracea L

Osteoporosis is a metabolic bone disease closely associated with oxidative stress. We had previously confirmed that the Viscozyme-assisted polysaccharide from Portulaca oleracea L (VPOP1) protects against antioxidant stress and evaluated the structure of VPOP1. In this study, we aimed to explore the anti-osteoporotic effects of VPOP1 on H2O2-induced osteoblast apoptosis. In addition, untargeted zebrafish metabolomics based on UPLC-Q-Orbitrap-HRMS was used to investigate the potential anti-osteoporotic mechanisms of VPOP1. The levels of Bcl-2 decreased significantly and those of caspase-3, Bax, and cytochrome C increased after treatment with H2O2. VPOP1 inhibited apoptosis in H2O2-induced MC3T3 cells. Metabolomic analyses showed that 28 potential biomarkers were gradually restored to normal levels after treatment with VPOP1 compared with that in the model group. Among them, leukotrienes D4 and A4, L-dopa, and L-tyrosine are important biomarkers and therapeutic targets. Pathway analysis revealed that arachidonic acid, tyrosine, phenylalanine, and sphingolipid metabolism were the major intervening pathways. Collectively, these results help us understand the protective activity of large molecular weight compounds, such as VPOP1, against osteoporosis.

Research into how carvacrol and metformin affect several human proteins in a hyperglycemic condition: A comparative study in silico and in vitro

Carvacrol (CV) is an organic compound found in the essential oils of many aromatic herbs. It is nearly unfeasible to analyze all the current human proteins for a query ligand using in vitro and in vivo methods. This study aimed to clarify whether CV possesses an anti-diabetic feature via Docking-based inverse docking and molecular dynamic (MD) simulation and in vitro characterization against a set of novel human protein targets. Herein, the best poses of CV docking simulations according to binding energy ranged from -7.9 to -3.5 (kcal/mol). After pathway analysis of the protein list through GeneMANIA and WebGestalt, eight interacting proteins (DPP4, FBP1, GCK, HSD11β1, INSR, PYGL, PPARA, and PPARG) with CV were determined, and these proteins exhibited stable structures during the MD process with CV. In vitro application, statistically significant results were achieved only in combined doses with CV or metformin. Considering all these findings, PPARG and INSR, among these target proteins of CV, are FDA-approved targets for treating diabetes. Therefore, CV may be on its way to becoming a promising therapeutic compound for treating Diabetes Mellitus (DM). Our outcomes expose formerly unexplored potential target human proteins, whose association with diabetic disorders might guide new potential treatments for DM.

A secreted proteomic footprint for stem cell pluripotency

With a view to developing a much-needed non-invasive method for monitoring the healthy pluripotent state of human stem cells in culture, we undertook proteomic analysis of the waste medium from cultured embryonic (Man-13) and induced (Rebl.PAT) human pluripotent stem cells (hPSCs). Cells were grown in E8 medium to maintain pluripotency, and then transferred to FGF2 and TGFβ deficient E6 media for 48 hours to replicate an early, undirected dissolution of pluripotency. We identified a distinct proteomic footprint associated with early loss of pluripotency in both hPSC lines, and a strong correlation with changes in the transcriptome. We demonstrate that multiplexing of four E8- against four E6- enriched secretome biomarkers provides a robust, diagnostic metric for the pluripotent state. These biomarkers were further confirmed by Western blotting which demonstrated consistent correlation with the pluripotent state across cell lines, and in response to a recovery assay.

Combining quantitative proteomics and interactomics for a deeper insight into molecular differences between human cell lines

Cellular functional pathways have evolved through selection based on fitness benefits conferred through protein intra- and inter-molecular interactions that comprise all protein conformational features and protein-protein interactions, collectively referred to as the interactome. While the interactome is regulated by proteome levels, it is also regulated independently by, post translational modification, co-factor, and ligand levels, as well as local protein environmental factors, such as osmolyte concentration, pH, ionic strength, temperature and others. In modern biomedical research, cultivatable cell lines have become an indispensable tool, with selection of optimal cell lines that exhibit specific functional profiles being critical for success in many cases. While it is clear that cell lines derived from different cell types have differential proteome levels, increased understanding of large-scale functional differences requires additional information beyond abundance level measurements, including how protein conformations and interactions are altered in certain cell types to shape functional landscapes. Here, we employed quantitative in vivo protein cross-linking coupled to mass spectrometry to probe large-scale protein conformational and interaction changes among three commonly employed human cell lines, HEK293, MCF-7, and HeLa cells. Isobaric quantitative Protein Interaction Reporter (iqPIR) technologies were used to obtain quantitative values of cross-linked peptides across three cell lines. These data illustrated highly reproducible (R2 values larger than 0.8 for all biological replicates) quantitative interactome levels across multiple biological replicates. We also measured protein abundance levels in these cells using data independent acquisition quantitative proteomics methods. Combining quantitative interactome and proteomics information allowed visualization of cell type-specific interactome changes mediated by proteome level adaptations as well as independently regulated interactome changes to gain deeper insight into possible drivers of these changes. Among the biggest detected alterations in protein interactions and conformations are changes in cytoskeletal proteins, RNA-binding proteins, chromatin remodeling complexes, mitochondrial proteins, and others. Overall, these data demonstrate the utility and reproducibility of quantitative cross-linking to study systems-level interactome variations. Moreover, these results illustrate how combined quantitative interactomics and proteomics can provide unique insight on cellular functional landscapes.

Advances in Integrated Multi-omics Analysis for Drug-Target Identification

As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.

The venom and telopodal defence systems of the centipede Lithobius forficatus are functionally convergent serial homologues

BACKGROUND

Evolution of novelty is a central theme in evolutionary biology, yet studying the origins of traits with an apparently discontinuous origin remains a major challenge. Venom systems are a well-suited model for the study of this phenomenon because they capture several aspects of novelty across multiple levels of biological complexity. However, while there is some knowledge on the evolution of individual toxins, not much is known about the evolution of venom systems as a whole. One way of shedding light on the evolution of new traits is to investigate less specialised serial homologues, i.e. repeated traits in an organism that share a developmental origin. This approach can be particularly informative in animals with repetitive body segments, such as centipedes.

RESULTS

Here, we investigate morphological and biochemical aspects of the defensive telopodal glandular organs borne on the posterior legs of venomous stone centipedes (Lithobiomorpha), using a multimethod approach, including behavioural observations, comparative morphology, proteomics, comparative transcriptomics and molecular phylogenetics. We show that the anterior venom system and posterior telopodal defence system are functionally convergent serial homologues, where one (telopodal defence) represents a model for the putative early evolutionary state of the other (venom). Venom glands and telopodal glandular organs appear to have evolved from the same type of epidermal gland (four-cell recto-canal type) and while the telopodal defensive secretion shares a great degree of compositional overlap with centipede venoms in general, these similarities arose predominantly through convergent recruitment of distantly related toxin-like components. Both systems are composed of elements predisposed to functional innovation across levels of biological complexity that range from proteins to glands, demonstrating clear parallels between molecular and morphological traits in the properties that facilitate the evolution of novelty.

CONCLUSIONS

The evolution of the lithobiomorph telopodal defence system provides indirect empirical support for the plausibility of the hypothesised evolutionary origin of the centipede venom system, which occurred through functional innovation and gradual specialisation of existing epidermal glands. Our results thus exemplify how continuous transformation and functional innovation can drive the apparent discontinuous emergence of novelties on higher levels of biological complexity.

Virtual screening of natural products to enhance melanogenosis

Natural products have long been an important source of inspiration for medicinal chemistry and drug discovery. In the cosmetic field, they remain the major elements of the composition and serve as marketing asset. Recent research showed the implication of salt-inducible kinases on the melanin production in skin via MITF regulation. Finding new potent modulators on such target could open the way to several cosmetic applications to attenuate visible signs of photoaging and improve the tan without sun. Since virtual screening can be a powerful tool for detecting hit compounds in the early stages of a drug discovery process, we applied this method on salt-inducible kinase 2 to discover potential interesting compounds. Here, we present the different steps from the construction of a database of natural products, to the validation of a docking protocol and the results of the virtual screening. Hits from the screening were tested in vitro to confirm their efficiency and results are discussed.

Pharmacophore modelling based virtual screening and molecular dynamics identified the novel inhibitors and drug targets against Waddlia chondrophila

Waddlia chondrophila is a possible cause of fetal death in humans. This Chlamydia-related bacterium is an emergent pathogen that causes human miscarriages and ruminant abortions, which results in financial losses. Despite the years of efforts, the underlying mechanism behind the pathogenesis of W. chondrophila is little known which hindered the development of novel treatment options. In the framework of current study, computational approaches were used to identify novel inhibitors (phytocompounds) and drug targets against W. chondrophila. At first, RNA polymerase sigma factor SigA and 3-deoxy-D-manno-octulosonic acid transferase were identified through subtractive proteomics pipeline. Afterwards, extensive docking and simulation analyses were conducted to optimize potentially novel phytocompounds by assessing their binding affinity to target proteins. A 100ns molecular dynamics simulation well complimented the compound's binding affinity and indicated strong stability of predicted compounds at the docked site. The calculation of binding free energies with MMGBSA corroborated the significant binding affinity between phytocompounds and target protein binding sites. The proposed phytocompounds may be a viable treatment option for patients infected with W. chondrophila; however, further research is required to ensure their safety.

Deep-learning-enabled antibiotic discovery through molecular de-extinction

Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. We trained ensembles of deep-learning models consisting of a peptide-sequence encoder coupled with neural networks for the prediction of antimicrobial activity and used it to mine 10,311,899 peptides. The models predicted 37,176 sequences with broad-spectrum antimicrobial activity, 11,035 of which were not found in extant organisms. We synthesized 69 peptides and experimentally confirmed their activity against bacterial pathogens. Most peptides killed bacteria by depolarizing their cytoplasmic membrane, contrary to known antimicrobial peptides, which tend to target the outer membrane. Notably, lead compounds (including mammuthusin-2 from the woolly mammoth, elephasin-2 from the straight-tusked elephant, hydrodamin-1 from the ancient sea cow, mylodonin-2 from the giant sloth and megalocerin-1 from the extinct giant elk) showed anti-infective activity in mice with skin abscess or thigh infections. Molecular de-extinction aided by deep learning may accelerate the discovery of therapeutic molecules.

A contribution to lipid digestion of Odobenidae family: Computational analysis of gastric and pancreatic lipases from walruses (Odobenus rosmarus divergens)

Triacylglycerols (TAGs) are a primary energy source for marine mammals during lipid digestion. Walruses (Odobenus rosmarus divergens) consume prey with a high content of long-chain polyunsaturated fatty acids; however, their digestive physiology and lipid digestion remain poorly studied. The present study aims to model and characterize the gastric (PWGL) and pancreatic (PWPL) lipases of Pacific walruses using an in-silico approach. The confident 3D models of PWGL and PWPL were obtained via homology modeling and protein threading and displayed the structural features of lipases. Molecular docking analysis demonstrated substrate selectivity for long-chain TAG (Trieicosapentaenoin; TC20:5n-3) in PWGL and short-chain TAG (Trioctanoin; TC8:0) in PWPL. Molecular dynamics simulations demonstrate that PWGL maintains structural stability at salinity conditions, with no significant conformational changes observed. In the simulations of PWGL bound to tridocosahexaenoin (TC22:6n-3), the protein is considerably stable at all three salinity conditions, but fluctuations are observed in the regions associated with catalytic sites and the lid, indicating the hydrolysis of the substrate. This is the first study to report on the digestion of TAGs in walruses, including modeling and lipases characterization and proposing a digestive tract for pinnipeds.

Paleolithic Gene Duplications Primed Adaptive Evolution of Human Amylase Locus Upon Agriculture

Starch digestion is a cornerstone of human nutrition. The amylase genes code for the starch-digesting amylase enzyme. Previous studies suggested that the salivary amylase (AMY1) gene copy number increased in response to agricultural diets. However, the lack of nucleotide resolution of the amylase locus hindered detailed evolutionary analyses. Here, we have resolved this locus at nucleotide resolution in 98 present-day humans and identified 30 distinct haplotypes, revealing that the coding sequences of all amylase gene copies are evolving under negative selection. The phylogenetic reconstruction suggested that haplotypes with three AMY1 gene copies, prevalent across all continents and constituting about 70% of observed haplotypes, originated before the out-of-Africa migrations of ancestral modern humans. Using thousands of unique 25 base pair sequences across the amylase locus, we showed that additional AMY1 gene copies existed in the genomes of four archaic hominin genomes, indicating that the initial duplication of this locus may have occurred as far back 800,000 years ago. We similarly analyzed 73 ancient human genomes dating from 300 - 45,000 years ago and found that the AMY1 copy number variation observed today existed long before the advent of agriculture (~10,000 years ago), predisposing this locus to adaptive increase in the frequency of higher amylase copy number with the spread of agriculture. Mechanistically, the common three-copy haplotypes seeded non-allelic homologous recombination events that appear to be occurring at one of the fastest rates seen for tandem repeats in the human genome. Our study provides a comprehensive population-level understanding of the genomic structure of the amylase locus, identifying the mechanisms and evolutionary history underlying its duplication and copy number variability in relation to the onset of agriculture.