Biology subjects

Hou, Q.

Publications and source records attributed to Hou, Q..

At least 19 recordsLinked to original sources

bioRxiv2025

Cortical microglia promote noradrenergic signaling to maintain wakefulness

Microglia are essential for maintaining brain homeostasis, including the sleep-wake cycle, but the underlying mechanisms remain unclear. Here, using in vivo two-photon imaging in both head-fixed and freely moving mice with simultaneous electroencephalogram-electromyogram (EEG-EMG) recording, we investigated microglial process dynamics and their interactions with locus coeruleus (LC) noradrenergic (NE) axons in the frontal cortex across sleep-wake states. During sleep or chemogenetic suppression of LC neurons, microglia enhanced their surveillance and increased interactions with NE axons. Correlative electron microscopy at nanometer resolution identified structural contacts between microglial process endings and NE boutons. Mechanistically, microglia-bouton interaction is primed by NE-{beta}2-adrenergic receptor ({beta}2AR) signaling, leading to enhanced Ca{superscript 2} activity in NE axons. These findings suggest that microglial interactions with NE axons promote cortical norepinephrine transmission and maintain stable wakefulness.

neuroscience↗

bioRxiv2025

Towards a Better Understanding of Batch Effects in Spatial Transcriptomics: Definition and Method Evaluation

BackgroundSpatial transcriptomics (ST) enables high-resolution mapping of gene expression within tissue slices, providing detailed insights into tissue architecture and cellular interactions. However, batch effects, arising from non-biological variations in sample collection, processing, sequencing platforms, or experimental protocols, can obscure biological signals, hinder data integration, and impact downstream analyses. Despite their critical impact, batch effects in ST datasets remain poorly defined and insufficiently explored. To address this gap, we propose a framework to categorize and define batch effects in ST and systematically evaluate the performance of ST methods with batch effect correction capabilities. ResultsWe categorized batch effects in ST into four types based on their sources: (1) Inter-slice, (2) Inter-sample, (3) Cross-protocol/platform, and (4) Intra-slice. Seven ST integration methods--DeepST, STAligner, GraphST, STitch3D, PRECAST, spatiAlign, and SPIRAL--were evaluated on benchmark datasets from human and mouse tissues. Using metrics such as graph connectivity, kBET, ASW, and iLISI, we assessed both the preservation of biological neighborhoods and the effectiveness of these methods in batch correction. Additionally, we applied STAligner for downstream analysis to compare results before and after batch correction, further highlighting the importance of batch effect correction in ST analysis. ConclusionNo single method is universally optimal. GraphST, PRECAST, SPIRAL, and STAligner performed well for same-platform integration, whereas SPIRAL and STAligner excelled in cross-platform settings. These findings highlight the need for robust and generalizable ST approaches with effective batch correction capabilities to facilitate the integration of multi-platform ST datasets in future research.

bioinformatics↗

bioRxiv2025

MNetClass: A Control-Free Microbial Network Clustering Framework for Identifying Central Subcommunities Across Ecological Niches

Investigating microbiome subnetworks and identifying central microbes in specific ecological niches is a critical issue in human microbiome studies. Traditional methods typically require control samples, limiting the ability to study microbiomes at distinct body sites without matched controls. Moreover, some clustering methods are not well-suited for microbial data and fail to identify central subcommunities across ecological niches after clustering. In this study, we present MNetClass, a novel microbial network clustering analysis framework. It utilizes a random walk algorithm and a rank-sum ratio-entropy weight evaluation model to classify key subnetworks and identify central microbes at any body site without the need for control samples. By applying MNetClass to microbiome data from five distinct oral sites, we successfully uncovered site-specific microbial subgroups and their central microbes. Additionally, simulations and Autism Spectrum Disorder (ASD) cohort datasets demonstrated that MNetClass outperforms existing unsupervised microbial clustering methods in terms of both accuracy and predictive power. In case studies, MNetClass identified age-related microbial communities across different oral sites, highlighting its broad applicability in microbiome research. IMPORTANCEMNetClass provides a valuable tool for microbiome network analysis, enabling the identification of key microbial subcommunities across diverse ecological niches. Implemented as an R package (https://github.com/YihuaWWW/MNetClass), it offers broad accessibility for researchers. Here, we systematically benchmarked MNetClass against existing microbial clustering methods across multiple datasets using various performance metrics, demonstrating its superior efficacy. Notably, MNetClass operates without the need for control groups and effectively identifies central microbes, highlighting its potential as a robust framework for advancing microbiome research.

bioinformatics↗

bioRxiv2024

The 11β-hydroxysteroid dehydrogenase type 1 inhibitor SPI-62 prevents morbidity in a mouse model of Cushing's syndrome

11{beta}-hydroxysteroid dehydrogenase type 1 (HSD-1) inhibitors represent a potential therapeutic approach for patients with Cushings syndrome, autonomous cortisol secretion (ACS), or iatrogenic glucocorticoid (GC) excess. We assessed whether the HSD-1 inhibitor SPI-62 prevents GC-associated morbidity in a mouse Cushings syndrome model. Corticosterone (CORT) was administered to mice for 5 weeks. Animals who received CORT were randomized between vehicle and 3 SPI-62 regimens. A control group received neither CORT nor SPI-62. Body weight was measured daily to enable weight-based SPI-62 administration. Food consumption by pair-housed animals was reported weekly. Insulin sensitivity was evaluated by fasting homeostatic model assessment of insulin resistance. Adiposity and skeletal myopathy were measured using magnetic resonance imaging and post-mortem weights of fat depots and skeletal muscles. Grip strength was measured with a digital meter. Ambulation behavior in an open field maze was assessed. Post-mortem dermal thickness was quantified. Skin structure was evaluated by histology. CORT was associated with decreased insulin sensitivity, increased adiposity, skeletal myoatrophy, reduced grip strength, decreased dermal thickness, and skin structural changes. A trend of increased food consumption and an unexpected biphasic weight change were observed with CORT. SPI-62 attenuated all observed adverse effects in a dose-dependent manner. In general, results for the high SPI-62 regimen were similar as those in animals who received no CORT, suggesting that full HSD-1 inhibition should be maintained throughout a dose interval to mitigate the effects of glucocorticoid excess.

pharmacology and toxicology↗

bioRxiv2024

BMP9 and BMP10 coordinate liver cellular crosstalk to maintain liver health

The liver is the largest solid organ in the body and is primarily composed of HCs, ECs, KCs, and HSCs, which spatially interact and cooperate with each other to maintain liver homeostasis. However, the complexity and molecular mechanisms underlying the crosstalk between these different cell types remain to be revealed. Here, we generated mice with conditional deletion of Bmp9/10 in different liver cell types and demonstrated that HSCs were the major source of BMP9 and BMP10 in the liver. Using transgenic ALK1 (receptor for BMP9/10) reporter mice, we found that ALK1 is expressed on KCs and ECs other than HCs and HSCs, and BMP9/10 secreted by HSCs promotes the differentiation of KCs and ECs and maintain their identity. Pdgfb expression was significantly upregulated in KCs and ECs after BMP9 and BMP10 deletion, ultimately leading to HSCs activation and liver fibrosis. ECs express several angiocrine factors, such as BMP2, BMP6, Wnt2 and Rspo3, to regulate hepatocyte iron metabolism and metabolic zonation. We found that these angiocrine factors were significantly decreased in ECs from Bmp9/10HSC-KO mice, which further resulted in liver iron overload and disruption of HC zonation. In summary, we demonstrated that HSCs play a central role in mediating liver cell-cell crosstalk via the production of BMP9/10, highlighting the important role of intercellular interaction in organ development and homeostasis.

cell biology↗

bioRxiv2023

A high-performance brain-sentence communication designed for logosyllabic language

Many severe neurological diseases, such as stroke and amyotrophic lateral sclerosis, can impair or destroy the ability of verbal communication. Recent advances in brain-computer interfaces (BCIs) have shown promise in restoring communication by decoding neural signals related to speech or motor activities into text. Existing research on speech neuroprosthesis has predominantly focused on alphabetic languages, leaving a significant gap of logosyllabic languages such as Mandarin Chinese which are spoken by more than 15% of the world population. Logosyllabic languages pose unique challenges to brain-to-text decoding due to extended character sets (e.g., 50,000+ for Mandarin Chinese) and complex mapping between characters and pronunciation. To address these challenges, we established a speech BCI designed for Mandarin, decoding speech-related stereoelectroencephalography (sEEG) signals into coherent sentences. We leverage the unique acoustic features of Mandarin Chinese syllables, constructing prediction models for syllable components (initials, tones, and finals), and employ a language model to resolve pronunciation to character ambiguities according to the semantic context. This method leads to a high-performance decoder with a median character accuracy of 71.00% over the full character set, demonstrating huge potentials for clinical application. To our knowledge, we are the first to report brain-to-sentence decoding for logosyllabic languages over full character set with a large intracranial electroencephalography dataset.

neuroscience↗

bioRxiv2023

Enzyme stability-activity trade-off: new insights from protein stability weaknesses and evolutionary conservation

A general limitation of the use of enzymes in biotechnological processes under sometimes non-physiological conditions is the complex interplay between two key quantities, enzyme activity and stability, where the increase of one is often associated with the decrease of the other. A precise stability-activity trade-off is necessary for the enzymes to be fully functional, but its weight in different protein regions and its dependence on environmental conditions is not yet elucidated. To advance this issue, we used the formalism that we have recently developed to effectively identify stability strength and weakness regions in protein structures, and applied it to a large set of globular enzymes with known experimental structure and catalytic sites. Our analysis showed a striking oscillatory pattern of free energy compensation centered on the catalytic region. Indeed, catalytic residues are usually non-optimal with respect to stability, but residues in the first shell around the catalytic site are, on the average, stability strengths and thus compensate for this lack of stability; residues in the second shell are weaker again, and so on. This trend is consistent across all enzyme families. It is accompanied by a similar, but less pronounced, pattern of residue conservation across evolution. In addition, we analyzed cold- and heat-adapted enzymes separately and highlighted different patterns of stability strengths and weaknesses, which provide insight into the longstanding problem of catalytic rate enhancement in cold environments. The successful comparison of our stability and conservation results with experimental fitness data, obtained by deep mutagenesis scanning, led us to propose criteria for improving catalytic activity while maintaining enzyme stability, a key goal in enzyme design.

bioinformatics↗

bioRxiv2023

Genome-scale enzymatic reaction prediction by variational graph autoencoders

BackgroundEnzymatic reaction networks are crucial to explore the mechanistic function of metabolites and proteins in biological systems and understanding the etiology of diseases and potential target for drug discovery. The increasing number of metabolic reactions allows the development of deep learning-based methods to discover new enzymatic reactions, which will expand the landscape of existing enzymatic reaction networks to investigate the disrupted metabolisms in diseases. ResultsIn this study, we propose the MPI-VGAE framework to predict metabolite-protein interactions (MPI) in a genome-scale heterogeneous enzymatic reaction network across ten organisms with thousands of enzymatic reactions. We improved the Variational Graph Autoencoders (VGAE) model to incorporate both molecular features of metabolites and proteins as well as neighboring features to achieve the best predictive performance of MPI. The MPI-VGAE framework showed robust performance in the reconstruction of hundreds of metabolic pathways and five functional enzymatic reaction networks. The MPI-VGAE framework was also applied to a homogenous metabolic reaction network and achieved as high performance as other state-of-art methods. Furthermore, the MPI-VGAE framework could be implemented to reconstruct the disease-specific MPI network based on hundreds of disrupted metabolites and proteins in Alzheimers disease and colorectal cancer, respectively. A substantial number of new potential enzymatic reactions were predicted and validated by molecular docking. These results highlight the potential of the MPI-VGAE framework for the discovery of novel disease-related enzymatic reactions and drug targets in real-world applications. Data availability and implementationThe MPI-VGAE framework and datasets are publicly accessible on GitHub https://github.com/mmetalab/mpi-vgae. Author BiographiesCheng Wang received his Ph.D. in Chemistry from The Ohio State Univesity, USA. He is currently a Assistant Professor in School of Public Health at Shandong University, China. His research interests include bioinformatics, machine learning-based approach with applications to biomedical networks. Chuang Yuan is a research assistant at Shandong University. He obtained the MS degree in Biology at the University of Science and Technology of China. His research interests include biochemistry & molecular biology, cell biology, biomedicine, bioinformatics, and computational biology. Yahui Wang is a PhD student in Department of Chemistry at Washington University in St. Louis. Her research interests include biochemistry, mass spectrometry-based metabolomics, and cancer metabolism. Ranran Chen is a master graduate student in School of Public Health at University of Shandong, China. Yuying Shi is a master graduate student in School of Public Health at University of Shandong, China. Gary J. Patti is the Michael and Tana Powell Professor at Washington University in St. Louis, where he holds appointments in the Department of Chemisrty and the Department of Medicine. He is also the Senior Director of the Center for Metabolomics and Isotope Tracing at Washington University. His research interests include metabolomics, bioinformatics, high-throughput mass spectrometry, environmental health, cancer, and aging. Leyi Wei received his Ph.D. in Computer Science from Xiamen University, China. He is currently a Professor in School of Software at Shandong University, China. His research interests include machine learning and its applications to bioinformatics. Qingzhen Hou received his Ph.D. in the Centre for Integrative Bioinformatics VU (IBIVU) from Vrije Universiteit Amsterdam, the Netherlands. Since 2020, He has serveved as the head of Bioinformatics Center in National Institute of Health Data Science of China and Assistant Professor in School of Public Health, Shandong University, China. His areas of research are bioinformatics and computational biophysics. Key pointsO_LIGenome-scale heterogeneous networks of metabolite-protein interaction (MPI) based on thousands of enzymatic reactions across ten organisms were constructed semi-automatically. C_LIO_LIAn enzymatic reaction prediction method called Metabolite-Protein Interaction Variational Graph Autoencoders (MPI-VGAE) was developed and optimized to achieve higher performance compared with existing machine learning methods by using both molecular features of metabolites and proteins. C_LIO_LIMPI-VGAE is broadly useful for applications involving the reconstruction of metabolic pathways, functional enzymatic reaction networks, and homogenous networks (e.g., metabolic reaction networks). C_LIO_LIBy implementing MPI-VGAE to Alzheimers disease and colorectal cancer, we obtained several novel disease-related protein-metabolite reactions with biological meanings. Moreover, we further investigated the reasonable binding details of protein-metabolite interactions using molecular docking approaches which provided useful information for disease mechanism and drug design. C_LI

bioinformatics↗

bioRxiv2022

Clean-PIE: a novel strategy for efficiently constructing precise circRNA with thoroughly minimized immunogenicity to direct potent and durable protein expression

Translatable circular RNAs (circRNAs) are emerging as a crucial molecular format for transient protein expression, with high potential to be an alternative for linear mRNA to reshape the landscape of mRNA pharmaceutical industry. Canonical Anabaena permuted intron-exon (Ana PIE) format that developed by ORNA is an efficient method for RNA circularization, and the engineered circRNAs direct supreme protein expression in eukaryotic cells. However, recent studies revealed that this method may unavoidably result in a remain of immunogenicity in the circRNA products, albeit after thorough RNA purification. In the current study, we develop a novel strategy for efficient generation of circRNA, via the permuted T4Td introns mediated autocatalytically ribozymatic reaction mediated ligation of the flanking segment sequences that concealing in ORF or translation initiation sequence (normally equal to IRES). This strategy universally realizes around 90% circularization effectivity, and the circRNA products can be purified to around 90% purity by our new purification method, and presented thoroughly minimized immunogenicity, thus is termed "Clean-PIE". The purified circRNAs are found to direct potent and durable expression of various proteins in vitro and in vivo. The partly purified Fluc circRNA by HPLC-SEC was found to direct Fluc expression in muscle for no less than 20 days. The highly purified circRNA exhibits much stronger protein expression in vitro and in vivo, and presumed a longer duration. Additionally, the scale-up of RNA circularization with the RNA precursors from 1 L transcription revealed high circularization effectivity (around 90%) and a high productivity of the final circRNA products. Collectively, Clean-PIE is a novel circRNA platform that possesses high circularization effectivity, enabled high RNA purity and thoroughly minimized immunogenicity, as well as scaling-up accessibility and directing extreme durability of protein expression, thus has the potential to develop advanced RNA vaccines and therapeutics in pharmaceutical industrial scale.

molecular biology↗

bioRxiv2022

Delivery of Circular mRNA via Degradable Lipid Nanoparticles against SARS-COV-2 Delta Variant

mRNA vaccines have emerged as a most promising and potent platform in the fight against various diseases including the COVID-19 pandemic. However, the intrinsic instability, varying side effects associated with the delivery systems, and continuous emergence of virus variants highlight the urgent need for the development of stable, safe and efficacious mRNA vaccines. In this study, by screening a panel of proprietary biodegradable ionizable lipidoids, we reported on a novel mRNA vaccine (cmRNA-1130) formed from a biodegradable lipidoid with eight ester bonds in the branched tail (AX4) and synthetic circular mRNA (cmRNA) encoding the trimeric Delta receptor binding domain (RBD) of SARS-CoV-2 spike protein for the induction of robust immune activation. The AX4-based lipid nanoparticles (AX4-LNP) revealed much faster elimination rate from liver and spleen in comparison with commercialized MC3-based LNP (MC3-LNP) and afforded normal level of alanine transferase (ALT), aspartate aminotransferase (AST), and creatinine (CRE) in BALB/c mice. Following intramuscular (IM) administration in BALB/c mice, cmRNA-1130 elicited potent and sustained neutralizing antibodies, RBD-specific CD4+ and CD8+ T effector memory cells (Tem), and Th1-biased T cell activations. cmRNA-1130 vaccine showed excellent stability against 6-month storage at 4 {degrees}C and freezing-thawing cycles. In brief, our study highlights mRNA vaccines based on cmRNA and biodegradable AX4 lipids hold great potential as superb therapeutic platforms for the treatment of varying diseases.

immunology↗

bioRxiv2022

Circular mRNA encoded PROTAC (RiboPROTAC) as a new platform for the degradation of intracellular therapeutic targets

Although proteolysis targeting chimera (PROTAC) technology that hijacking the ubiquitin-proteasome system (UPS) to artificially degrade protein is an emerging promising technology for many undruggable targets, it still faces several challenges, such as the difficulty to select high specificity molecule to protein of interest (POI), and only few of the E3-ligase are suitable for PROTAC mediated protein degradation. Protein-based PROTAC, termed BioPROTAC, owns the advantage of specificity but lacks membrane permeability. Here, we develop a new targeted protein degrading platform, which we termed RiboPROTAC, by encoding BioPROTAC protein degraders with circular mRNA (cmRNA) and delivering intracellularly. In this way, intracellular protein GFP and nuclear protein PCNA were successfully degraded. For PCNA targeting, not only the level of target protein is strongly decreased, but also the cell function related is strongly interfered, such as cell proliferation, apoptosis, as well as the cell cycle. What is more, for the first time, we observed that degrading PCNA with RiboPROTAC causes immunogenic cell death (ICD), although not strong, but was verified in vivo. Importantly, our in vivo results demonstrated that cmRNA encoded PCNA degrader exhibited significant tumor suppression effect, thus we provide the first proof of concept for the application of RiboPROTAC as potential mRNA therapeutic. We consider that RiboPROTAC is a new and superior PROTAC technology for targeting the undruggable targets.

molecular biology↗

bioRxiv2021

Intratumoral Delivered Novel Circular mRNA Encoding Cytokines for Immune Modulation and Cancer Therapy

The application of mRNA as a novel kind of vaccine has been proved recently, due to the emergence use authorization (EUA) by FDA for the two COVID-19 mRNA vaccines developed by Moderna and BioNTech. Both of the two vaccines are based on canonical linear mRNA, and encapsulated by lipid nanoparticle (LNP). Circular mRNA, which is found to mediate potent and durable protein expression, is an emerging technology recently. Owing to its simplicity of manufacturing and superior performance of protein expression, circular mRNA is believed to be a disruptor for mRNA area. However, the application of circular mRNA is still at an initiation stage, proof of concept for its usage as future medicine or vaccine is necessary. In the current study, we established a novel kind of circular mRNA, termed C-RNA, based on Echovirus 29 (E29)-derived internal ribosome entry sites (IRES) and newly designed homology arms and RNA spacers. Our results demonstrated that this kind of circular mRNA is able to mediate strong and durable protein expression, compared to typical linear mRNA. Moreover, for the first time, our study demonstrated that direct intratumoral administration of C-RNA encoding a mixture of cytokines achieved successful modulation of intratumoral and systematic anti-tumor immune responses and finally leading to an enhancement of anti-PD-1 antibody-induced tumor repression in syngeneic mouse model. Additionally, after an optimization of the circular mRNA formulation, a significant improvement of C-RNA mediated protein expression was observed. With this optimized formulation, C-RNA induced enhanced anti-tumor effect via intratumoral administration and elicited significant activation of tumor-infiltrated total T cells and CD8+ T cells. Collectively, we established C-RNA, a novel circular mRNA platform, and demonstrated that it can be applied for direct intratumoral administration for cancer therapy.

cancer biology↗

bioRxiv2020

SeRenDIP-CE: Sequence-based Interface Prediction for Conformational Epitopes

MotivationAntibodies play an important role in clinical research and biotechnology, with their specificity determined by the interaction with the antigens epitope region, as a special type of protein-protein interaction (PPI) interface. The ubiquitous availability of sequence data, allows us to predicting epitopes from sequence in order to focus time-consuming wet-lab experiments onto the most promising epitope regions. Here, we extend our previously developed sequence-based predictors for homodimer and heterodimer PPI interfaces to predict epitope residues that have the potential to bind an antibody. ResultsWe collected and curated a high quality epitope dataset from the SAbDaB database. Our generic PPI heterodimer predictor obtained an AUC-ROC of 0.666 when evaluated on the epitope test set. We then trained a random forest model specifically on the epitope dataset, reaching AUC 0.694. Further training on the combined heterodimer and epitope datasets, improves our final predictor to AUC 0.703 on the epitope test set. This is better than the best state-of-the-art sequence-based epitope predictor BepiPred-2.0. On one solved antibody-antigen structure of the COVID19 virus spike RNA binding domain, our predictor reaches AUC 0.778. We added the SeRenDIP-CE Conformational Epitope predictors to our webserver, which is simple to use and only requires a single antigen sequence as input, which will help make the method immediately applicable in a wide range of biomedical and biomolecular research. AvailabilityWebserver, source code and datasets are available at www.ibi.vu.nl/programs/serendipwww/ Contactk.a.feenstra@vu.nl

bioinformatics↗

bioRxiv2020

SWOTein: A structure-based approach to predict stability Strengths and Weaknesses of prOTEINs

MotivationAlthough structured proteins adopt their lowest free energy conformation in physiological conditions, the individual residues are generally not in their lowest free energy conformation. Residues that are stability weaknesses are often involved in functional regions, whereas stability strengths ensure local structural stability. The detection of strengths and weaknesses provides key information to guide protein engineering experiments aiming to modulate folding and various functional processes. ResultsWe developed the SWOTein predictor which identifies strong and weak residues in proteins on the basis of three types of statistical energy functions describing local interactions along the chain, hydrophobic forces and tertiary interactions. The large-scale comparison of the different types of strengths and weaknesses showed their complementarity and the enhancement of the information they provide. We applied SWOTein to apocytochrome b562 and found good agreement between predicted strengths and weaknesses and native hydrogen exchange data. Its application to an amino acid-binding protein identified the hinge at the basis of the conformational change. SWOTein is both fast and accurate and can be applied at small and large scale to analyze and modulate folding and molecular recognition processes. AvailabilityThe SWOTein webserver provides the list of predicted strengths and weaknesses and a protein structure visualization tool that facilitates the interpretation of the predictions. It is freely available for academic use at http://babylone.ulb.ac.be/SWOTein.

bioinformatics↗

bioRxiv2020

Microbiota in milk from healthy and mastitis cows varies greatly in diversity, species richness and composition, as revealed by PacBio sequencing

Mastitis is the most economically important disease of dairy cows. This study used PacBio single-molecule real-time sequencing technology to sequence the full-length of the l6S rRNA from the microbiota in 27 milk samples (18 from mastitis and 9 from healthy cows; the cows were at different stages of lactation). We observed that healthy or late stage milk microbiota had significantly higher microbial diversity and richness. The community composition of the microbiota from different groups also varied greatly. In milk from healthy cows the microbiota was predominantly comprised of Lactococcus lactis, Acinetobacter johnsonii and Bacteroides dorei, while from mastitis cows it was predominantly comprised of Bacillus cereus, Clostridium cadaveris and Streptococcus suis. The prevalence of La. lactis and B. cereus in milk from healthy and mastitis cows was confirmed by digital droplets PCR. Differences in milk microbiota composition could suggest an important role for these microbes in protecting the host from mastitis. Based on the milk microbiota profiles, the Udder Health Index was constructed to predict the risk of bovine mastitis. Application of this predictive model could aid early identification and prevention of mastitis in dairy cows, though the model requires further optimisation using a larger dataset.

microbiology↗

bioRxiv2019

A comprehensive computational study of amino acid interactions in membrane proteins

Transmembrane proteins play a fundamental role in a wide series of biological processes but, despite their importance, they are less studied than globular proteins, essentially because their embedding in lipid membranes hampers their experimental characterization. In this paper, we improved our understanding of their structural stability through the development of new knowledge-based energy functions describing amino acid pair interactions that prevail in the transmembrane and extramembrane regions of membrane proteins. The comparison of these potentials and those derived from globular proteins yields an objective view of the relative strength of amino acid interactions in the different protein environments, and their role in protein stabilization. Separate potentials were also derived from -helical and {beta}-barrel transmembrane regions to investigate possible dissimilarities. We found that, in extramembrane regions, hydrophobic residues are less frequent but interactions between aromatic and aliphatic amino acids as well as aromatic-sulfur interactions contribute more to stability. In transmembrane regions, polar residues are less abundant but interactions between residues of equal or opposite charges or non-charged polar residues as well as anion-{pi} interactions appear stronger. This shows indirectly the preference of the water and lipid molecules to interact with polar and hydrophobic residues, respectively. We applied these new energy functions to predict whether a residue is located in the trans- or extramembrane region, and obtained an AUC score of 83% in cross validation, which demonstrates their accuracy. As their application is, moreover, extremely fast, they are optimal instruments for membrane protein design and large-scale investigations of membrane protein stability.

bioinformatics↗

bioRxiv2019

SOLart: a structure-based method to predict protein solubility and aggregation

MotivationThe solubility of a protein is often decisive for its proper functioning. Lack of solubility is a major bottleneck in high-throughput structural genomic studies and in high-concentration protein production, and the formation of protein aggregates causes a wide variety of diseases. Since solubility measurements are time-consuming and expensive, there is a strong need for solubility prediction tools.\n\nResultsWe have recently introduced solubility-dependent distance potentials that are able to unravel the role of residue-residue interactions in promoting or decreasing protein solubility. Here, we extended their construction by defining solubility-dependent potentials based on backbone torsion angles and solvent accessibility, and integrated them, together with other structure- and sequence-based features, into a random forest model trained on a set of E. coli proteins with experimental structures and solubility values. We thus obtained the SOLart protein solubility predictor, whose most informative features turned out to be folding free energy differences computed from our solubility-dependent statistical potentials. SOLart performances are very good, with a Pearson correlation coefficient between experimental and predicted solubility values of 0.7 both in the training dataset and on an independent set of S. Cerevisiae proteins. On test sets of modeled structures, only a limited drop in performance is observed. SOLart can thus be used with both high-resolution and low-resolution structures, and clearly outperforms state-of-art solubility predictors. It is available through a user-friendly webserver, which is easy to use by non-expert scientists.\n\nAvailabilityThe SOLart webserver is freely available at babylone.ulb.ac.be/SOLART/

bioinformatics↗

bioRxiv2018

Metatranscriptome profiling of the dynamic transcription of mRNA and sRNA of a probiotic Lactobacillus strain in human gut

Metatranscriptomic sequencing has recently been applied to study how pathogens and probiotics affect human gastrointestinal (GI) tract microbiota, which provides new insights into their mechanisms of action. In this study, metatranscriptomic sequencing was applied to deduce the in vivo expression patterns of an ingested Lactobacillus casei strain, which was compared with its in vitro growth transcriptomes. Extraction of the strain-specific reads revealed that transcripts from the ingested L. casei were increased, while those from the resident L. paracasei strains remained unchanged. Mapping of all metatranscriptomic reads and transcriptomic reads to L. casei genome showed that gene expression in vitro and in vivo differed dramatically. About 39% (1163) mRNAs and 45% (93) sRNAs of L. casei well-expressed were repressed after ingested into human gut. Expression of ABC transporter genes and amino acid metabolism genes was induced at day-14 of ingestion; and genes for sugar and SCFA metabolisms were activated at day-28 of ingestion. Moreover, expression of sRNAs specific to the in vitro log phase was more likely to be activated in human gut. Expression of rli28c sRNA with peaked expression during the in vitro stationary phase was also activated in human gut; this sRNA repressed L. casei growth and lactic acid production in vitro. These findings implicate that the ingested L. casei might have to successfully change its transcription patterns to survive in human gut, and the time-dependent activation patterns indicate a highly dynamic cross-talk between the probiotic and human gut including its microbe community.\n\nImportanceProbiotic bacteria are important in food industry and as model microorganisms in understanding bacterial gene regulation. Although probiotic functions and mechanisms in human gastrointestinal tract are linked to the unique probiotic gene expression, it remains elusive how transcription of probiotic bacteria is dynamically regulated after being ingested. Previous study of probiotic gene expression in human fecal samples has been restricted due to its low abundance and the presence of of closely related species. In this study, we took the advantage of the good depth of metatranscriptomic sequencing reads and developed a strain-specific read analysis method to discriminate the transcription of the probiotic Lactobacillus casei and those of its resident relatives. This approach and additional bioinformatics analysis allowed the first study of the dynamic transcriptome profiles of probiotic L casei in vivo. The novel findings indicate a highly regulated repression and dynamic activation of probiotic genome in human GI tract.

microbiology↗