Biology subjects

Yilmaz, S.

Publications and source records attributed to Yilmaz, S..

At least 19 recordsLinked to original sources

bioRxiv2025

ENGINEERING AND PRECLINICAL EVALUATION OF DRIED FOOD-FORMULATED YEAST-SECRETED ANTIBODIES AS PERI-EXPOSURE PROPHYLAXIS AGAINST CLOSTRIDIOIDES DIFFICILE DISEASE.

Clostridioides difficile gastrointestinal infection (CDI) causes significant disease burden, often following gut dysbiosis induced by antibiotic treatment in at-risk individuals. A non-invasive, easy to use targeted prophylaxis in such cases of acute elevated CDI risk, especially in those with a history of recurrent CDI, would prevent much suffering. In this study, we envisioned a simple prophylactic intervention for CDI enabled by the oral administration of yeast-secreted dried food-formulated C. difficile toxin A (TcdA)-and B (TcdB)-neutralizing VHH-based antibodies. We engineered two antibody versions: a previously reported tetravalent VHH (VHH4) comprising dual specificities for TcdA and TcdB, and its murine IgA Fc fusion counterpart (VHH4-mIgA Fc). We explored Generally Recognized as Safe (GRAS) Komagataella phaffii yeast as a low-cost, highly scalable production platform. In a first experiment, we produced the antibodies at small scale in shake flask yeast cultivations and formulated the secreted antibody-containing growth medium into standard-composition dried mouse feed pellets. The antibody food pellet-administration intervention was tested in a C. difficile spore-challenged mouse model in which CDI leads to 50% mortality of untreated animals. We started the antibody-feed prophylaxis dosed at 10 nmol/g pellet one day prior to C. difficile challenge and continued until the infection was cleared with ad libitum feeding. We found that the VHH4 feed almost completely prevented mortality and reduced disease-related weight loss. Despite its higher in vitro potency against TcdA and equipotent potency against TcdB, the VHH4-mIgA Fc fusion did not confer additional in vivo benefit, and further work focused on the simpler VHH4 format. Scalable high-cell-density bioreactor production of VHH4 yielded equally effective prophylactic formulations at the same dose of 10 nmol/g pellet, achieving complete survival and reduced disease burden. Our findings suggest that, with further antibody- and process development, oral prophylaxis with scalable, cost-effective, yeast-produced, dried food-formulated anti-toxin VHHs could offer a simple non-invasive, non-antibiotic prophylaxis option for the high-risk (r)CDI patient population.

molecular biology↗

bioRxiv2025

Centriolar satellites are dynamic membrane-less organelles that assemble via a hierarchical pathway

Centriolar satellites (CS) are ubiquitous, membrane-less organelles recognized for organelle crosstalk, plasticity, diverse functions and links to developmental and neuronal diseases. However, the molecular principles governing their assembly and regulation remain poorly understood. To address this, we developed cellular and in vitro biogenesis assays that allow spatiotemporal quantification of CS granule properties during assembly, remodeling and maintenance. Using these tools, we show that CS assemble via a hierarchical pathway initiated by PCM1 scaffold formation followed by regulated client recruitment. PCM1 intrinsically assembles into granules through multimerization, a process modulated by cytoskeleton. High-resolution imaging revealed that PCM1 and its clients occupy distinct subdomains with different compositions and dynamics, adding an additional layer of regulation. Perturbing PCM1 multimerization impaired ciliary signaling, underscoring its functional importance. Collectively, these findings define the molecular basis of CS biogenesis, establish new tools to probe their context-dependent functions, and provide a framework for understanding how CS deregulation contributes to disease. More broadly, the principles uncovered here may extend to other membrane-less organelles, explaining their specificity and plasticity.

cell biology↗

bioRxiv2025

Variants in glycine decarboxylase activate mechanisms of mitochondrial energy metabolism in the brain.

Brain energy metabolism is produced from glucose by mitochondrial oxidative phosphorylation. Variants in the mitochondrial enzyme glycine decarboxylase (GLDC) cause a rare neurological disease, non-ketotic hyperglycinemia (NKH), with expected hallmarks of brain glycine elevation and responsiveness to folate deficiency that are equivalent to the severity of Gldc mutations. We remarkably find that brains of young- attenuated mutant mice with a 1.5-fold increase in glycine are reduced > 5-fold in GLDC, show a decline in both the mitochondrial lipoyl-transfer protein GCSH and lipoylation of the pyruvate dehydrogenase (PDH) complex, as well as concomitant rise in signatures of astrocyte mitochondrial {beta}-oxidation of fatty acids and activation of neuronal PDH. Our findings suggest a novel GLDC mechanism of remodeling mitochondrial energy systems throughout the brain, established early in and sustained throughout post-natal NKH disease.

neuroscience↗

bioRxiv2025

Implantable CMOS Deep-Brain Fluorescence Imager with Single-Neuron Resolution

Despite the advantages of optical imaging over electrophysiology, such as cell-type specificity, its application has been limited to the investigation of shallow brain regions (< 2 mm) because of the light scattering property of brain tissue. Passive optical conduits such as graded-index lenses and waveguides have permitted access to deeper locales but with restricted resolution and field of view, while creating massive lesions along the inserted path, with little pathway to improvement in the technology. As an alternative, we present the Acus device, an active implantable complementary metal-oxide-semiconductor (CMOS) neural imager with a 512-pixel silicon image sensor post- processed into a 4.1-mm-long, 120-m-wide shank with a collinear fiber for illumination, which is able to record transient fluorescent signals in deep brain regions at 400 frames/sec. Acus can achieve single-neuron resolution in functional imaging of GCaMP6s-expressing neurons at a frame rate of 400 frames/sec.

neuroscience↗

bioRxiv2024

System-level characterization of engineered and evolved formatotrophic E. coli strains

One-carbon compounds, such as formate, are promising and sustainable feedstocks for microbial bioproduction of fuels and chemicals. Growth of Escherichia coli on formate was recently achieved by introducing the reductive glycine pathway (rGlyP) into its genome, which is theoretically the most energy-efficient aerobic formate assimilation pathway. While adaptive laboratory evolution was used to enhance the growth rate and biomass yield significantly, still the best performing formatotrophic E. coli strain did not approach the theoretical optimal biomass yield of the rGlyP. In this study, we investigated these previously engineered formatotrophic E. coli strains to find out why the biomass yield was sub-optimal and how it may be improved. Through a combination of metabolic modelling, genomic and proteomic analysis, we identified several potential metabolic bottlenecks and future targets for optimization. This study also reveals further insights in the evolutionary mutations and related changes in proteome allocation that supported the already substantially improved growth of formatotrophic E. coli strain. This systems-level analysis provides key insights to realize high-yield, fast growing formatotrophic strains for future bioproduction.

synthetic biology↗

bioRxiv2024

Multi-Targeted Effects of Novel Cycloastragenol Derivatives: Enhancing NRF2, Proteostasis, and Telomerase Pathways with p53 Modulation to Delay Replicative Senescence

Aging is a complex, multifactorial process driven by various cellular and molecular mechanisms, including telomere shortening, oxidative stress, and the decline of proteostasis, all of which contribute to replicative senescence and age-related diseases. Cycloastragenol (CA), a triterpenoid saponin derived from Astragalus membranaceus, has shown potential for its ability to activate telomerase, suggesting therapeutic benefits in delaying cellular aging. In this study, we explored the effects of novel CA derivatives, obtained through biotransformation as telomerase activators, on the NRF2/proteasome/telomerase axis and their potential to delay replicative senescence in human primary epidermal keratinocytes (HEKn). Our findings reveal that these CA derivatives significantly enhance NRF2 nuclear activity, leading to the upregulation of key cytoprotective enzymes essential for mitigating oxidative stress. Notably, these derivatives exhibited efficacy at much lower concentrations compared to CA, demonstrating their potential for enhanced therapeutic application. The derivatives also markedly increased proteasome activity, particularly in the {beta}1 and {beta}5 subunits, thereby preserving proteostasis--a critical factor in preventing the accumulation of damaged proteins associated with aging. Furthermore, continuous treatment with these derivatives sustained stimulatory effects, which was evidenced by increased NRF2, proteasome, and hTERT protein levels even in senescent cells and extended cellular lifespan. Additionally, we explored the impact of CA derivatives on p53-mediated pathways, demonstrating that these compounds effectively modulate the p53/p21 axis, reducing cell cycle arrest and promoting cellular proliferation. Moreover, the derivatives exhibited neuroprotective properties by attenuating glutamate-induced excitotoxicity, further underscoring their potential as multi-targeted anti-aging agents. In conclusion, our study provides strong evidence that novel CA derivatives act on multiple fronts to enhance NRF2 activity, maintain proteostasis, and modulate telomerase and p53 pathways, most at lower doses compared to CA. These actions collectively contribute to the delay of replicative senescence and the promotion of cellular longevity, positioning CA derivatives as potent candidates for developing multi-targeted anti-aging therapies that address the complex interplay of aging-related cellular processes. HighlightsO_LITelomerase-active CA derivatives enhance NRF2 activity and proteasome activity, leading to cytoprotection at lower doses than CA. C_LIO_LICA derivatives modulate the p53 pathway and cell cycle, prolonging cellular lifespan and delaying replicative senescence. C_LIO_LICA derivatives protect cells against glutamate-excitotoxicity along with decreased p53 protein levels. C_LI

biochemistry↗

bioRxiv2024

Multi-Drug Artificial Neural Network Models for Predicting HIV-1 RTI Resistance

Drug resistance is a major barrier to effectively treating HIV/AIDS, necessitating the exploration of novel drugs and an understanding of resistance mechanisms. Genotypic and phenotypic tests, while common, are costly and time-consuming. To address these challenges, machine learning models that effectively generalize available data have been leveraged. This study aims to create multi-drug artificial neural network (MD-ANN) models based on drug-isolate-fold change (DIF) to predict the drug resistance profiles of HIV-1 reverse transcriptase inhibitors (RTIs) using the Stanford HIV Drug Resistance Database. Unlike existing isolate-fold change (IF) models, the DIF-based models can test novel RTIs against a given mutant strain. It has been shown that the DIF-based models have competitive predictive capabilities in single-drug benchmarks compared to IF-based models while taking advantage of molecular learning to test novel RTIs. The DIF-based models can rank ten FDA-approved inhibitor pairs according to their drug resistance scores. We validate our observations with an external test set consisting of drug resistance scores for novel RTIs in the CheMBL database. By combining the Stanford dataset and the external dataset, our DIF model achieves impressive classification and regression results with an accuracy of 0.774, AUC of 0.859, and r of 0.701 on the test set. To demonstrate how our DIF models learn from drug molecules, we construct a null model that only takes into account isolate representations. This null model yields an accuracy of 0.641, AUC of 0.701, and r of 0.470. We highlight the significant contribution of drug information in improving the predictive accuracy and reliability of the DIF model. The DIF models have the potential to facilitate the testing of new inhibitors across various isolates.

bioinformatics↗

bioRxiv2023

Exploring Temporal and Sex-Linked Dysregulation in Alzheimer's Disease Phospho-Proteome

This study aims to characterize dysregulation of phosphorylation for the 5XFAD mouse model of Alzheimers disease (AD). Employing global phosphoproteome measurements, we analyze temporal (3, 6, 9 months) and sex-dependent effects on mouse hippocampus tissue to unveil molecular signatures associated with AD initiation and progression. Our results indicate 1.9 to 4.4 times higher phosphorylation prevalence compared to protein expression across all time points, with approximately 4.5 times greater prevalence in females compared to males at 3 and 9 months. Moreover, our findings reveal consistent phosphorylation of known AD biomarkers APOE and GFAP in 5XFAD mice, alongside novel candidates BIG3, CLCN6 and STX7, suggesting their potential as biomarkers for AD pathology. In addition, we identify PDK1 as a significantly dysregulated kinase at 9 months in females, and the regulation of gap junction activity as a key pathway associated with Alzheimers disease across all time points. AD-Xplorer, the interactive browser of our dataset, enables exploration of AD-related changes in phosphorylation, protein expression, kinase activities, and pathways. AD-Xplorer aids in biomarker discovery and therapeutic target identification, emphasizing temporal and sex-specific nature of significant phosphoproteomic signatures. Available at: https://yilmazs.shinyapps.io/ADXplorer HighlightsO_LIPhosphorylation-level dysregulation surpasses protein expression C_LIO_LIHigher phospho-dysregulation in females, starting as early as 3-month time point C_LIO_LINovel candidates BIG3, CLCN6, and STX7 exhibit consistent phospho-dysregulation C_LIO_LIDeveloped AD-Xplorer: Online tool to explore Alzheimers disease phospho-proteome C_LI In BriefThis study investigates dysregulation of phospho-proteome in an Alzheimers disease (AD) mouse model, identifying consistent phosphorylation of established AD biomarkers APOE and GFAP, along with novel candidate biomarkers BIG3, CLCN6, and STX7. In addition, the study observes significant PDK1 dysregulation at 9 months, particularly in females. AD-Xplorer, our interactive tool for exploring temporal and sex-linked phosphorylation changes, protein expression, kinase activities, and pathway enrichment, empowers researchers to gain deeper insights into AD mechanisms and uncover novel biomarkers and therapeutic targets.

neuroscience↗

bioRxiv2022

Enzyme levels broadly influence flux throughout the metabolic network

Metabolic reaction flux is regulated in response to nutritional, environmental or pathological conditions by changes in either metabolite or metabolic enzyme levels. Previous studies proposed that flux is predominately regulated by metabolite, rather than enzyme, levels. However, the extent to which changes in enzyme levels affect flux throughout the metabolic network remains unclear. Here, we combine available yeast enzyme level, flux data, and metabolic network modeling to demonstrate three paradigms by which enzyme levels are broadly associated with flux: cognate reaction, pathway-level coordination, and flux coupling. We find that the architecture of the metabolic network enables the reach of influence for most enzymes. We implemented enzyme reach as a novel parameter in an enhanced flux potential analysis algorithm, which predicts relative flux levels under different conditions from variations in enzyme expression. This algorithm was tested in yeast and humans. Our study suggests that metabolic network architecture facilitates a broad physiological impact of changes in enzyme levels and may form a foundation for using enzyme expression data for a variety of systems, and eventually, individual cells.

systems biology↗

bioRxiv2022

Systems-level transcriptional regulation of Caenorhabditis elegans metabolism

Metabolism is precisely controlled to ensure organismal development and homeostasis. Several mechanisms regulate metabolism, including allosteric control and transcriptional regulation of metabolic enzymes and transporters. So far, metabolism regulation has mostly been described for individual genes and pathways, and the extent of transcriptional regulation of the entire metabolic network remains largely unknown. Here, we find that more than three-quarters of all metabolic genes are transcriptionally regulated in the nematode Caenorhabditis elegans. We find that many annotated metabolic pathways are coexpressed, and we use gene expression data and the iCEL1314 metabolic network model to define coregulated sub-pathways in an unbiased manner. Using a large gene expression compendium, we determine the conditions where sub-pathways exhibit strong coexpression. Finally, we develop WormClust, a web application that enables a gene-by-gene query of genes to view their association with metabolic (sub)-pathways. Overall, this study sheds light on the ubiquity of transcriptional regulation of metabolism and provides a blueprint for similar studies in other organisms, including humans.

systems biology↗

bioRxiv2022

Are under-studied proteins under-represented? How to fairly evaluate link prediction algorithms in network biology

For biomedical applications, new link prediction algorithms are continuously being developed and these algorithms are typically evaluated computationally, using test sets generated by sampling the edges uniformly at random. However, as we demonstrate, this evaluation approach introduces a bias towards "rich nodes", i.e., those with higher degrees in the network. More concerningly, this bias persists even when different network snapshots are used for evaluation, as recommended in the machine learning community. This creates a cycle in research where newly developed algorithms generate more knowledge on well-studied biological entities while under-studied entities are commonly overlooked. To overcome this issue, we propose a weighted validation setting specifically focusing on under-studied entities and present AWARE strategies to facilitate bias-aware training and evaluation of link prediction algorithms. These strategies can help researchers gain better insights from computational evaluations and promote the development of new algorithms focusing on novel findings and under-studied proteins. TeaserSystematically characterizes and mitigates bias toward well-studied proteins in the evaluation pipeline for machine learning. Code and data availabilityAll materials (code and data) to reproduce the analyses and figures in the paper is available in figshare (doi:10.6084/m9.figshare.21330429). The code for the evaluation framework implementing the proposed strategies is available at github{dagger}. We provide a web tool{ddagger} to assess the bias in benchmarking data and to generate bias-adjusted test sets.

systems biology↗

bioRxiv2022

Prediction of Kinase-Substrate Associations Using The Functional Landscape of Kinases and Phosphorylation Sites

MotivationProtein phosphorylation is a key post-translational modification that plays a central role in many cellular processes. With recent advances in biotechnology, thousands of phosphorylated sites can be identified and quantified in a given sample, enabling proteome-wide screening of cellular signaling. However, the kinase(s) that phosphorylate most (> 90%) of the identified phosphorylation sites are unknown. Knowledge of kinase-substrate associations is also mostly limited to a small number of well-studied kinases, with 20% of known kinases accounting for the phosphorylation of 87% of currently annotated sites. The scarcity of available annotations calls for the development of computational algorithms for more comprehensive and reliable prediction of kinase-substrate associations. ResultsTo broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations, we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships and associations among phosphosites and kinases. To construct a phosphosite-phosphosite association network, we use sequence similarity, shared biological pathways, co-evolution, co-occurrence, and co-phosphorylation of phosphosites across different biological states. To construct a kinase-kinase association network, we integrate protein-protein interactions, shared biological pathways, and membership in common kinase families. We use node embeddings computed from these heterogeneous networks to train machine learning models for predicting kinase-substrate associations. Our systematic computational experiments using the PhosphositePLUS database shows that the resulting algorithm, NetKSA, outperforms state-of-the-art algorithms and resources, including KinomeXplorer and LinkPhinder, in reliably predicting KSAs. By stratifying the ranking of kinases, NetKSA also enables annotation of phosphosites that are targeted by relatively less-studied kinases. Finally, we observe that the performance of NetKSA is robust to the choice of network embedding algorithms, while each type of network contributes valuable information that is complementary to the information provided by other networks. ConclusionRepresentation of available functional information on kinases and phosphorylation sites, along with integrative machine learning algorithms, has the potential to significantly enhance our knowledge on kinase-substrate associations. AvailabilityThe code and data are available at compbio.case.edu/NetKSA.

bioinformatics↗

bioRxiv2022

The role of Cycloastragenol at the intersection of Nrf-2/ARE, telomerase, and proteasome activity

Aging is well-characterized by the gradual decline of cellular functionality. As redox balance, proteostasis, and telomerase systems have been found to be associated with aging and age-related diseases, targeting these systems by small compounds has been considered as a promising therapeutic approach. Cycloastragenol (CA), a small molecule telomerase activator obtained from Astragalus species, has been reported to have positive effects on several age-related pathophysiologies, including ischemia, glucose intolerance, diabetes, and neurodegenerative diseases. Although CA has received intense attention as a promising compound for aging and age-related diseases, the mechanisms underlying CA activity related to redox balance, proteasome, and telomerase has not yet been reported. Here, we presented that CA increased Nrf-2 activity leading to upregulation of cytoprotective enzymes and attenuation of oxidative stress-induced ROS levels. Furthermore, CA enhanced telomerase activity by increasing not only the expression of hTERT but also its nuclear localization via the Hsp90-chaperon complex. CA alleviated the oxidative stress-induced mitochondrial hTERT levels while increasing the nuclear hTERT levels. Additionally, the proteasome activity and assembly were increased by CA. Strikingly, our data revealed that CA-mediated neuroprotection requires both Nrf-2 and hTERT induction. In conclusion, this study is the first report describing the effect of CA on these aging-related three major cellular pathways and their interrelationships. As the proteasome activator effect of CA is dependent on induction of telomerase activity, which is mediated by Nrf-2 system, CA has a great potential for healthier aging and prevention or treatment of age-related diseases by positively affecting these three cellular pathways.

cell biology↗

bioRxiv2022

Whole-genome sequencing of Bacillus thuringiensis strain SY49.1 reveals the detection of novel candidate pesticidal and bioactive compounds isolated from Turkey

BackgroundBacillus thuringiensis SY49.1 (Bt SY49.1) strain is a new strain isolated from a soil sample in Adana city which is nestled in the most fertile agricultural area in Turkey. This strain has insecticidal activity against insect pests from different orders. Also, it is characterized by its significant activity against plant fungal infections and as plant growth promotion. AimsTo report the whole genome sequencing (WGS) and annotation of the Bt SY49.1 strain. Materials and MethodsThe Bt SY49.1 strain was isolated from the soil sample in Adana city by using a sodium acetate enriched medium. Bacterial DNA was extracted then sequenced using Illumina HiSeq technology. After data filtering and quality control, de novo assembly and genomic annotation were applied. Evolutionary and comparative genomic analysis and variant calling were performed using various in silico tools. ResultsThe WGS of strain Bt SY49.1 is 6. 32 Mbp long with a GC content of 34.68%. It reveals large numbers of single nucleotide polymorphisms (SNPs) and InDels. The assembled genome contains 6,562 protein-encoding genes. In addition, it encodes various gene inventories for the biosynthesis of bioactive compounds such as insecticidal delta endotoxins, growth stimulatory deaminase and phosphatases, fungicidal thuricin, bacillibactin, petrobactin, fengycin / plipastin, and zwittermycin A. ConclusionThe strain Bt SY49.1 could have several potential utilities as a source of antibiotics compounds, plant growth promoting metabolites, and biocontrol agents for fungal phytopathogens, and insects. We expect that the draft genome of the Bt SY49.1 strain may provide a model for proper understanding and studying of antimicrobial compound mining, genetic diversity among the B. cereus group, and pathogenicity against insect pests and plant diseases, and growth-promoting activity.

genomics↗

bioRxiv2021

New Insights into Molecular Basis Identification of Three Novel Strains of the Bacillus Subtilis Group Produce Cry Proteins Isolated from Soil Samples in Adana, Turkey

AimsThis study aimed to analyze the evolutionary relationship between Bacillus species isolated from agricultural soil using in-silico tools. Methods and ResultsAcross-sectional study was conducted in Adana province, in Turkey. A total of 120 Bacillus species were isolated from 80 soil samples. However, the phylogenetic tree diverged into two lineages; one belongs to B. subtilis group while the other belongs to B. cereus group. Interestingly, three native strains (SY27.1A, SY35.3A, and SY58.5A), which produce Cry proteins, shared high similarity with B. subtilis group (over 99%) and less than 95% similarity with known B. thuringiensis and other species of B. cereus group. Furthermore, 11 canonical SNPs (canSNPs) were identified in strains that belong to B. pumilus group when compared with B. subtilis reference sequences. ConclusionsPhylogenetic analysis of 16S rRNA sequences was found valuable for differentiation between Bacillus species isolated from soil samples. In addition, SNPs analysis provided more intra-specific information in the cases of B. subtilis group. Significance and Impact of StudyA detailed analysis was provided for the SNPs present in a conserved region of 16S rRNA gene of Bacillus species. Also, we proposed three novel Bacillus strains that produce Cry proteins and belong to B. subtilis group.

microbiology↗

bioRxiv2021

Constructing Functional Networks of Phosphorylation Sites Using Co-Phosphorylation

Protein phosphorylation is a ubiquitous regulatory mechanism that plays a central role in cellular signaling. According to recent estimates, up to 70% of human proteins can be phosphorylated. Therefore, characterization of phosphorylation dynamics is critical for understanding a broad range of biological and biochemical processes. Technologies based on mass spectrometry are rapidly advancing to meet the needs for high-throughput screening of phosphorylation. These technologies enable untargeted quantification of thousands of phosphorylation sites in a given sample. Many labs are already utilizing these technologies to comprehensively characterize signaling landscapes by examining perturbations with drugs and knockdown approaches, or by assessing diverse phenotypes in cancers, neuro-degerenational diseases, infectious diseases, and normal development. Here, we comprehensively investigate the concept of "co-phosphorylation", defined as the correlated phosphorylation of a pair of phosphosites across various biological states. We integrate nine publicly available phospho-proteomics datasets for various diseases (including breast cancer, ovarian cancer and Alzhemiers disease) and utilize functional data related to sequence, evolutionary histories, kinase annotations, and pathway annotations to investigate the functional relevance of co-phosphorylation. Our results across a broad range of studies consistently show that functionally associated sites tend to exhibit significant positive or negative co-phosphorylation. Specifically, we show that co-phosphorylation can be used to predict with high precision the sites that are on the same pathway or that are targeted by the same kinase. Overall, these results establish co-phosphorylation as a useful resource for analyzing phospho-proteins in a network context, which can help extend our knowledge on cellular signaling and its dysregulation.

systems biology↗

bioRxiv2021

Induction of cross-reactive antibody responses against the RBD domain of the spike protein of SARS-CoV-2 by commensal microbiota

The commensal microflora is a source for multiple antigens that may induce cross-reactive antibodies against host proteins and pathogens. However, whether commensal bacteria can induce cross-reactive antibodies against SARS-CoV-2 remains unknown. Here we report that several commensal bacteria contribute to the generation of cross-reactive IgA antibodies against the receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein. We identified SARS-CoV-2 unexposed individuals with RBD-binding IgA antibodies at their mucosal surfaces. Conversely, neutralising monoclonal anti-RBD antibodies recognised distinct commensal bacterial species. Some of these bacteria, such as Streptococcus salivarius, induced a cross-reactive anti-RBD antibodies upon supplementation in mice. Conversely, severely ill COVID-19 patients showed reduction of Streptococcus and Veillonella in their oropharynx and feces and a reduction of anti-RBD IgA at mucosal surfaces. Altogether, distinct microbial species of the human microbiota can induce secretory IgA antibodies cross-reactive for the RBD of SARS-CoV-2.

microbiology↗

bioRxiv2020

Uncovering complementary sets of variants for the prediction of quantitative phenotypes

MotivationGenome-wide association studies show that variants in individual genomic loci alone are not sufficient to explain the heritability of complex, quantitative phenotypes. Many computational methods have been developed to address this issue by considering subsets of loci that can collectively predict the phenotype. This problem can be considered a challenging instance of feature selection in which the number of dimensions (loci that are screened) is much larger than the number of samples. While currently available methods can achieve decent phenotype prediction performance, they either do not scale to large datasets or have parameters that require extensive tuning. ResultsWe propose a fast and simple algorithm, Macarons, to select a small, complementary subset of variants by avoiding redundant pairs that are in linkage disequilibrium. Our method features two interpretable parameters that control the time/performance trade-off without requiring parameter tuning. In our computational experiments, we show that Macarons consistently achieves similar or better prediction performance than state-of-the-art selection methods while having a simpler premise and being at least 2 orders of magnitude faster. Overall, Macarons can seamlessly scale to the human genome with ~107 variants in a matter of minutes while taking the dependencies between the variants into account. ConclusionMacarons can offer a reasonable trade-off between phenotype predictivity, runtime and the complementarity of the selected subsets. The framework we present can be generalized to other high-dimensional feature selection problems within and beyond biomedical applications. AvailabilityMacarons is implemented in Matlab and the source code is available at: https://github.com/serhan-yilmaz/macarons

bioinformatics↗