Biology subjects

Jabbari, H.

Publications and source records attributed to Jabbari, H..

18 recordsLinked to original sources

bioRxiv2026

Honey bee (Apis mellifera) gut microbiome associations with viruses and pesticides across Canadian agroecosystems

The honey bee (Apis mellifera) gut microbiome plays a central role in host health, yet its variation across agricultural landscapes remains poorly resolved. This study investigates how major environmental stressors, particularly pesticide exposure and RNA virus loadings, shape the honey bee gut microbiome in a large-scale field study conducted across Canada, spanning diverse agroecosystems from British Columbia to Quebec. We identify consistent associations between specific bacterial taxa and major RNA viruses, including enrichment of Serratia marcescens with SBV and depletion of Bombella intestini with BQCV. Pesticide exposure is likewise linked to reproducible shifts in key microbial taxa. Together, these findings reveal that interacting stressors jointly shape the bee gut microbiome and enable prediction of microbiome responses in agroecosystems. HighlightsDistinct associations identified between gut bacteria and major bee RNA viruses (BQCV, SBV, LSV, IAPV) Pesticide exposure is linked to reproducible shifts in key microbial taxa Combined virus-pesticide effects form coordinated clusters that predict microbiome variation and specific bacterial responses Integrated modeling demonstrates that environmental stressors can jointly explain microbiome structure beyond crop effects Graphical abstractSchematic overview of potential links between pesticide exposure and RNA virus infection and their effects on the bee gut bacterial community. Solid arrows indicate associations supported by the present study, whereas dashed arrows indicate hypothesized or unresolved interactions. Associations between the presence of specific bee RNA viruses (left) or pesticide residues (right) and changes in the relative abundance of particular gut taxa (pink {uparrow}, increased; blue {downarrow}, decreased). The pesticide subtype is indicated by the icon in the cell (leaf - herbicide, hyphae - fungicide and insect - insecticide). Several bacterial taxa showed reproducible associations with specific viral or pesticide variables, including Bombella intestini, Serratia marcescens, Melissococcus plutonius, Paenibacillus alvei, Apibacter sp. wkB309, and Gilliamella sp. A7. Abbreviations: BQCV Black queen cell virus; LSV, Lake Sinai virus; SBV, Sacbrood virus; IAPV, Israeli acute paralysis virus. (p/n/b) indicate the sample matrix in which the pesticide was detected, namely pollen, nectar, and bee tissue, respectively. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/731697v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@cb92a4org.highwire.dtl.DTLVardef@1087045org.highwire.dtl.DTLVardef@102cabforg.highwire.dtl.DTLVardef@4ce2f1_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2026

Comparative Proteomics Across Tissues and Crop Agroecosystems Reveals Agricultural Stressor Responses in the Western Honey Bee

Maintaining honey bee health in crop production systems is increasingly difficult because worker bees encounter multiple chemical and biological pressures from pesticides and pathogens. How these field-realistic pressures affect molecular physiology across functionally distinct tissues remains poorly understood. Here, we tested whether tissue-resolved proteomics could separate stable tissue-specific patterns from crop-associated molecular changes. To do this, we profiled abdomen, gut, and head proteomes from honey bees collected across four Canadian crop ecosystems over two consecutive years, and integrated these data with pesticide-residue and pathogen-load measurements. Proteomic variation was structured by both tissue identity and crop environment. Tissue-specific proteomic profiles were characterized across samples, whereas crop-associated effects were detected in both years and were stronger in 2021, the second year of the study. Tissue-specific enrichment and network analyses linked the abdomen to lipid catabolism and ubiquitin-proteasome proteostasis, the gut to central carbon metabolism, membrane transport, vesicle trafficking, and cytoskeletal organization, and the head to neurosensory and mitochondrial functions, together with amino-sugar metabolism and vesicle-associated quality-control modules. Among the measured pesticide residues, boscalid was the most reproducible chemical correlate of proteomic variation, with the strongest signal in the gut. Cross-year validation associated boscalid exposure with reduced abundance of gut proteins involved in mitochondrial metabolism, protein quality control, vesicle trafficking, nutrient transport, and biosynthetic pathways. Additionally, integrated proteome-transcriptome-microbiome factor analysis further identified gut-centered components associated with measured stressor variables and linked protein-level variation to coordinated transcriptomic and microbial shifts. Independent-year validation showed that compact crop-associated protein signatures detected in 2020 were also present in 2021. Together, these results show that honey bee tissues maintain stable proteomic identities while showing tissue- and year-specific responses to pesticide and pathogen pressures encountered in crop ecosystems. The gut proteome may specifically provide a sensitive molecular indicator of pesticide-associated perturbation under field conditions.

bioinformatics↗

bioRxiv2026

CoSTAR: Coarse Stem-Topology Alignment of Pseudoknotted RNA Structures by Relation-Constrained Search

RNA structural alignment is a central task in comparative RNA analysis, but many efficient methods achieve tractability by restricting the class of admissible structures, often excluding pseudoknots. This exclusion is limiting for viral and regulatory RNAs, where conserved structure can remain informative even when sequence conservation is weak. We introduce a coarse RNA structural alignment algorithm that aligns secondary structures by searching over partial maps between stems rather than nucleotides. Each input structure is decomposed into stems, annotated with nucleotide-level features, and encoded by pairwise topological relations among stems. Alignment is formulated as a cost-minimizing partial stem map with skip operations, and the search tree is pruned by RNA-specific directionality and topological constraints derived from already aligned stems. For the stated cost function and over the class of injective, direction-preserving, topologically consistent stem maps, the search is exact. This shifts the dominant computational dependence from sequence length to the number and arrangement of stems. We evaluated the method on 2100 pairwise alignments sampled from seven Rfam families spanning 40-224 nucleotides and 2-15 stems. Across these benchmarks, the algorithm returned terminal coarse alignments in which every stem was either matched or skipped. We measured running time and search-tree width to characterize performance on diverse family-to-family comparisons. The experiments also show that ordering the input structures affects efficiency: using the structure with more stems as the search-driving structure reduces tree width. The resulting partial stem map is directly interpretable for RNA annotation and can be projected to nucleotide resolution for downstream sequence-structure analysis. The source code for CoSTAR is available at: https://github.com/TheCOBRALab/CoSTAR

bioinformatics↗

bioRxiv2025

Stable, intronic RNAs explain preservation of introns in Cyanidioschyzon merolae

Despite recent work identifying functional roles for introns, we lack a broad understanding of why some introns are preserved while others are removed. Here, we use the thermophilic red alga, Cyanidioschyzon merolae, as a model to investigate why only 39 of the approximately 2000 ancestral introns were preserved in this lineage. We observe that 23 of the 39 introns encode stable RNAs, 11 of which represent a novel class of non-coding RNA (ncRNA), which we call stable intronically-encoded RNAs (sieRNAs). These novel ncRNAs are expressed constitutively under normal growth conditions and are conserved in other extremophilic algae. One sieRNA, Q270, is predicted to stabilize a chloroplast polycistronic transcript encoding 17 ribosomal protein genes through direct antisense base pairing. Strikingly, all sieRNAs are polyadenylated under heat stress with long tails ranging from 50-200 nucleotides long, linking them to a potential heat stress response that may be critical for heat adaptation. Furthermore, DMS-MaP chemical probing revealed that some sieRNAs contain three-way junctions, a common RNA regulatory element, while others undergo accessibility shifts between in vivo and in vitro conditions, indicative of cellular interactions. Our findings suggest that introns in C. merolae are preserved to encode ncRNAs and suggest that introns may serve as hosts for regulatory RNAs across eukaryotes.

molecular biology↗

bioRxiv2025

Evaluation of network inference algorithms for derivation of metabolic relationships from lipidomic and metabolomic datasets

MotivationVarious methods have been proposed to construct metabolic networks from metabolomic data; however, small sample sizes, multiple confounding factors, the presence of indirect interactions as well as randomness in metabolic processes are of major concern. ResultsIn this study, we benchmark existing algorithms for creating correlation- and regression-based networks of changes in metabolite abundance and we evaluate their performance across different sample sizes of a generative model. Using standard interaction-level tests and network-scale analyses based on centrality scores, we assess how well these methods capture simulated metabolomic networks. Our findings reveal significant challenges in network inference and result interpretation, even when sample sizes are significant and data are the result of computer modeling of metabolic pathways. Despite these limitations, we demonstrate that correlation-based network inference can, to some extent, discriminate between two different metabolic states. This suggests potential utility in distinguishing overarching changes in metabolic processes but not direct pathways in different conditions. AvailabilityAll relevant data is provided at https://github.com/HosnaJabbari/metabolicRelationships Contactjabbari@ualberta.ca Supplementary informationSupplementary data are available at Bioinformatics Advances

bioinformatics↗

bioRxiv2025

Missense mutation knowledge can decrease prediction inaccuracies on protein secondary structure

Protein tertiary structure prediction models like AlphaFold2 have revolutionized the field with unprecedented accuracy. Yet predicting structural changes arising from single amino acid mutations remains a challenge. The complexity introduced by these mutations calls for models that can incorporate mutational information into their predictions. We propose a novel refinement strategy for protein secondary structure prediction that leverages missense mutational data. As part of this strategy, we introduce Mut2Dens, a model that not only yields improved consistency of predictions for mutational data, but also maintains robust predictive performance on non-mutational datasets. Mut2Dens takes multiple predicted secondary structures and generates a mutation-aware secondary structure. This awareness comes from our mutational dataset, learning to avoid common mistakes in prediction methods after a missense mutation occurs. In particular, Mut2Dens employs the extremely randomized trees (ExtraTree) algorithm to avoid overfitting and makes effective use of the limited mutational data available from experimentally determined three-dimensional structures. By combining predictions from highly accurate structure prediction models, we create an ensemble that integrates their strengths while enhancing mutational capabilities. This refinement strategy also improves the non-mutational performance of state-of-the-art methods by addressing their most inaccurate and least confident predictions. Moreover, it reduces improbable outcomes in mutated protein structures--such as transforming{pi} -helices into {beta}-sheets--that can still occur in current prediction models. Finally, by using interpretable machine learning algorithms (e.g., ExtraTree), we can reveal the underlying biological knowledge from the refinement model; the insights gained from Mut2Dens can be corroborated with known mutational outcomes, helping users pinpoint discrepancies across structure prediction models and make more informed decisions regarding the predicted structures. The data utilized here is available at https://github.com/ivanpmartell/sam-models.

bioinformatics↗

bioRxiv2024

Missense mutations: Backbone structure positional effects

Human diversity often manifests through single nucleotide polymorphisms (SNPs). Among these, missense mutations, or SNPs that alter amino acids, can modify a proteins three-dimensional (3D) structure. This impacts its function and can potentially elicit diseases or affect drug interactions. Thus, understanding protein single point mutations is crucial for precision medicine, as it helps tailor treatments based on individual genetic variations. As atomic locations can be susceptible to any number of changes that might or might not affect function, we focus on the secondary structure to provide concrete results on possible protein structural deformation that may occur from missense mutations. We assess state-of-the-art structure prediction methods regarding backbone deformations caused by missense mutations. We categorize these deformations as local, distant, or global based on the proximity of structural changes to the mutation site. Our analysis utilizes a diverse dataset from the Protein Data Bank, comprising over 500 protein clusters with experimentally determined structures and documented mutations. Our findings indicate that missense mutations can significantly affect the accuracy of structure prediction methods. These mutations often lead to predicted structural changes even when the actual secondary structures remain unchanged, suggesting that current methods overestimate the impact of missense mutations. This issue is particularly evident in advanced prediction algorithms, which struggle to accurately model proteins with stable mutations. We also found that the addition of low-performing prediction methods during structural analysis can positively impact the results on some proteins, particularly those with low homology. Furthermore, proteins that form complexes or bind ligands--such as membrane and transport proteins--are inaccurately predicted due to the absence of extra-molecular interaction data in the models, highlighting how missense mutations can complicate accurate structure prediction. All code and data are available at https://github.com/ivanpmartell/pdb-sam.

bioinformatics↗

bioRxiv2024

Markers, Mechanisms and Metrics of Biological Aging: A Scoping Review

Biological aging is a rapidly growing area of research, which entails characterizing the rate of aging independent of an individuals chronological age. In this review, we analyze the results of biological aging research in 435 papers published in a twelve year window, revealing changing patterns of molecular markers of biological aging use over time, and the development of novel metrics of biological aging. We further identify consistent and discordant research findings, as well as areas of potential future research focusing on questions of measurement with methylation or biomarker-based assessment and other variables relevant to the study of biological age.

molecular biology↗

bioRxiv2024

Structural impact of synonymous mutations in six SARS-CoV-2 Variants of Concern

SARS-CoV-2 continues to spread and infect people worldwide. While most effort into characterizing variants of this virus have focused on non-synonymous changes, accumulation of synonymous mutations in different viral variants has also occurred. Here we characterize six Variants of Concern in terms of their mutational content, and make predictions regarding the impact of those mutations on potential genomic RNA secondary structure and stability. We find that synonymous mutations typically have no or modest impact to RNA secondary structure. As synonymous mutations are free from the selective pressure imposed on protein-altering mutations, the impact of synonymous mutations is largely limited to RNA secondary structure considerations. The absence of major, structure-altering synonymous mutations emphasize the importance of RNA structure, including within coding regions, to viral fitness.

bioinformatics↗

bioRxiv2024

A method to identify high consensus predictions of single-cell metabolic flux

Altered metabolism is a key contributor to pathology in numerous disease states, including cancer. These changes can occur within certain pathological cells, or within a population of cells. Two recently developed single-cell flux prediction tools, Single-cell Flux Estimation Analysis ("scFEA") and Compass, have shown success in predicting cellular metabolism using readily available transcriptome data. By adapting the outputs of these tools, we sought to determine if they can work in concert to identify higher confidence consensus flux predictions. We created a set of reaction modules for the two systems. By testing multiple function composites with sets of modularized Compass outputs, we identified a method that showed the highest global similarity to the outputs of scFEA. Our analysis showed broad biological areas of agreement between the results of the two systems when applied to single-cell data arising from both pathological and healthy samples, with pathological samples increasing system consensus. Consensus testing on matched transcriptome and metabolomics data suggested that agreement between the two systems could indicate at least a minimal degree of coherence between both systems and direct metabolite measurements. Overall, we demonstrated that automated Comparisons between the outputs of Compass and scFEA are possible, applicable to data arising from pathological samples, and that such a consensus approach can reveal strongly correlated predictions between these two systems. Author summaryStudying the metabolism of individual cells allows us to understand the mechanisms behind a myriad of diseases. However, single-cell metabolism cannot readily be measured. Computational tools exist to predict metabolism, but validating their outputs requires metabolic measurements. To address this circular shortcoming, we created a method to automatically adapt and compare the outputs of two popular systems used to predict single-cell metabolism from genetic data. In other fields, using predictive methods in an ensemble has provided superior accuracy, and we speculated that the same may hold true for computational predictions of single-cell metabolism. Our work demonstrated that these two systems can be used together to find agreement on a broad range of metabolic processes related to disease. Further, our results, although early, suggest that system agreement may indicate genuine shifts in the underlying biology of a cell population. Owing to the methodologies used by the two systems, such changes could be studied at both a broad or granular level. As our comparison tools provide rapid readouts of such system agreement, they could potentially be used as part of an exploratory pipeline to aid in identification of candidate metabolic mechanisms as drug targets.

bioinformatics↗

bioRxiv2023

Tying the Knot: Unraveling the Intricacies of the Coronavirus Frameshift Pseudoknot

Understanding and targeting functional RNA structures towards treatment of coronavirus infection can help us to prepare for novel variants of SARS-CoV-2 (the virus causing COVID-19), and any other coronaviruses that could emerge via human-to-human transmission or potential zoonotic (inter-species) events. Leveraging the fact that all coronaviruses use a mechanism known as -1 programmed ribosomal frameshifting (-1 PRF) to replicate, we apply algorithms to predict the most energetically favourable secondary structures (each nucleotide involved in at most one pairing) that may be involved in regulating the -1 PRF event in coronaviruses, especially SARS-CoV-2. We compute previously unknown most stable structure predictions for the frameshift site of coronaviruses via hierarchical folding, a biologically motivated framework where initial non-crossing structure folds first, followed by subsequent, possibly crossing (pseudoknotted), structures. Using mutual information from 181 coronavirus sequences, in conjunction with the algorithm KnotAli, we compute secondary structure predictions for the frameshift site of different coronaviruses. We then utilize the Shapify algorithm to obtain most stable SARS-CoV-2 secondary structure predictions guided by frameshift sequence-specific and genome-wide experimental data. We build on our previous secondary structure investigation of the singular SARS-CoV-2 68 nt frameshift element sequence, by using Shapify to obtain predictions for 132 extended sequences and including covariation information. Previous investigations have not applied hierarchical folding to extended length SARS-CoV-2 frameshift sequences. By doing so, we simulate the effects of ribosome interaction with the frameshift site, providing insight to biological function. We contribute in-depth discussion to contextualize secondary structure dual-graph motifs for SARS-CoV-2, highlighting the energetic stability of the previously identified 3 8 motif alongside the known dominant 3 3 and 3 6 (native-type) -1 PRF structures. Integrating experimental data within minimum free energy (MFE) hierarchical folding algorithms provides novel structure predictions to distill the relationship between RNA structure and function. In particular, fully categorizing most stable secondary structure predictions via hierarchical folding supports our identification of motif transitions and critical site targets for future therapeutic research. Author summaryFinding evolutionary connections between coronaviruses frameshift element RNA structures is a worthwhile goal in contributing to treatment development for afflicted human and animal populations. Predicting the most energetically favourable RNA secondary structures, and how they may form via the hierarchical folding hypothesis, is an efficient use of computational resources to shed light on RNA structure-function. We used the KnotAli algorithm to obtain mutual information from 181 coronaviruses frameshift RNA sequences. Guided by this evolutionary information, we computed secondary structure predictions to allow comparison of marked similarities and subtle differences between SARS-CoV-2 and other coronaviruses frameshift element RNA structures. In addition, we applied the Shapify algorithm to predict secondary structures for extended SARS-CoV-2 frameshift element sequences informed by SHAPE reactivity data. Here we critically expand the known landscape of most stable -1 PRF secondary structure conformations, isolating the location of key secondary structure motif transitions that can improve site targeting of viral therapeutics. Our application of hierarchical folding algorithms contributes novel predictions of functional RNA structures, enhancing discussion of how secondary structures unfold or refold to regulate frameshifting in coronaviruses.

bioinformatics↗

bioRxiv2023

SparseRNAFolD: Sparse RNA pseudoknot-free Folding including Dangles

MotivationComputational RNA secondary structure prediction by free energy minimization is indispensable for analyzing structural RNAs and their interactions. These methods find the structure with the minimum free energy (MFE) among exponentially many possible structures and have a restrictive time and space complexity (O(n3) time and O(n2) space for pseudoknot-free structures) for longer RNA sequences. Furthermore, accurate free energy calculations including dangles contributions can be difficult and costly to implement, particularly when optimizing for time and space requirements. ResultsHere we introduce a fast and efficient sparsified MFE pseudoknot-free structure prediction algorithm, SparseRNAFolD, that utilizes an accurate energy model that accounts for dangles contributions. While sparsification technique was previously employed to improve time and space complexity of a pseudoknot-free structure prediction method with a realistic energy model, SparseMFEFold, it was not extended to include dangles contributions due to complexity of computation. This may be at the cost of prediction accuracy. In this work, we compare three different sparsified implementations for dangles contributions and provide pros and cons of each method. As well, we compare our algorithm to LinearFold, a linear time and space algorithm, where we find in practice, SparseRNAFolD has lower memory consumption across all lengths of sequence and a faster time for lengths up to 1000 bases. ConclusionOur SparseRNAFolD algorithm is an MFE-based algorithm that guarantees optimality of result and employs the most general energy model including dangles contributions. We provide basis for applying dangles to sparsified recursion in a pseudoknot-free model which has the ability to be extended to pseudoknots. AvailabilitySparseRNAFolDs algorithm and detailed results are available at https://github.com/mateog4712/SparseRNAFolD.

bioinformatics↗

bioRxiv2023

CParty: Conditional partition function for density-2RNA pseudoknots

Biologically relevant RNA secondary structures are routinely predicted by efficient dynamic programming algorithms that minimize their free energy. Starting from such algorithms, one can devise partition function algorithms, which enable stochastic perspectives on RNA structure ensembles. As most prominent example McCaskills partition function algorithm is derived from pseudoknot-free energy minimization. While this algorithm became hugely successful for the stochastic analysis of pseudoknot-free RNA structure, as of yet there exists only one pseudoknotted partition function implementation, which covers only simple pseudoknots and comes with a borderline-prohibitive complexity of O(n5) in the RNA length n. In this article, we develop a partition function algorithm corresponding to the hierarchical pseudoknot prediction of HFold, which performs exact optimization in a realistic pseudoknot energy model. In consequence, our algorithm CParty carries over HFolds advantages over classical pseudoknot prediction to stochastic analysis. In only cubic time, it computes the hierarchically constrained partition function over pseudoknotted density-2 structures G {cup} G', composed of pseudoknot-free parts G and G', where G is given. Thus, it follows the common hypothesis of hierarchical pseudoknot formation, where pseudoknots form as tertiary contacts only after a first pseudoknot-free core G. Like HFold, CParty is very efficient, achieving the low complexity of the pseudoknot-free algorithm. Finally, by computing pseudoknotted ensemble energies, we unveil kinetics features of a therapeutic target in SARS-CoV-2. AvailibilityCParty is available at https://github.com/HosnaJabbari/CParty.

bioinformatics↗

bioRxiv2022

Prediction of RNA secondary structures in SARS-CoV-2 and comparison with contemporary predictions

AO_SCPLOWBSTRACTC_SCPLOWSARS-CoV-2, the causative agent of covid-19, is known to exhibit secondary structure in its 5 and 3 untranslated regions, along with the frameshifting stimulatory element situated between ORF1a and 1b. To identify further regions containing conserved structure, multiple sequence alignment with related coronaviruses was used as a starting point from which to apply a modified computational pipeline developed to identify non-coding RNA elements in vertebrate eukaryotes. Three different RNA structural prediction approaches were employed in this modified pipeline. Forty genomic regions deemed likely to harbour structure were identified, ten of which exhibited three-way consensus substructure predictions amongst our predictive utilities. Intracomparison of the pipelines predictive utilities, along with intercomparison with three previously published SARS-CoV-2 structural datasets, were performed. Limited agreement as to precise structure was observed, although different approaches appear to agree upon regions likely to contain structure in the viral genome.

molecular biology↗

bioRxiv2021

Supervised Promoter Recognition: A Benchmark Framework

AO_SCPLOWBSTRACTC_SCPLOWDeep learning has become a prevalent method in identifying genomic regulatory sequences such as promoters. In a number of recent papers, the performance of deep learning models have continually been reported as an improvement over alternatives for sequence-based promoter recognition. However, the performance improvements in these models do not account for the different datasets that models are being evaluated on. The lack of a consensus dataset and procedure for benchmarking purposes has made the comparison of each models true performance difficult to assess. We present a framework called Supervised Promoter Recognition Framework ( SUPR REF) capable of streamlining the complete process of training, validating, testing, and comparing promoter recognition models in a systematic manner. SUPR REF includes the creation of biologically relevant benchmark datasets to be used in the evaluation process of deep learning promoter recognition models. We showcase this framework by comparing the models performance on alternative datasets, and properly evaluate previously published models on new benchmark datasets. Our results show that the reliability of deep learning ab initio promoter recognition models on eukaryotic genomic sequences is still not at a sufficient level, as precision is severely lacking. Furthermore, given the observational nature of these data, cross-validation results from small datasets need to be interpreted with caution. AvailabilitySource code and documentation of the framework is available online at https://github.com/ivanpmartell/suprref

bioinformatics↗

bioRxiv2020

SARS-CoV-2 ribosomal frameshifting pseudoknot: Improved secondary structure prediction and detection of inter-viral structural similarity

AO_SCPLOWBSTRACTC_SCPLOWSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the COVID-19 pandemic; a pandemic of a scale that has not been seen in the modern era. Despite over 29 million reported cases and over 900, 000 deaths worldwide as of September 2020, herd immunity and widespread vaccination efforts by many experts are expected to be insufficient in addressing this crisis for the foreseeable future. Thus, there is an urgent need for treatments that can lessen the effects of SARS-CoV-2 in patients who become seriously affected. Many viruses including HIV, the common cold, SARS-CoV and SARS-CoV-2 use a unique mechanism known as -1 programmed ribosomal frameshifting (-1 PRF) to successfully replicate and infect cells in the human host. SARS-CoV (the coronavirus responsible for SARS) and SARS-CoV-2 possess a unique RNA structure, a three-stemmed pseudoknot, that stimulates -1 PRF. Recent experiments identified that small molecules can be introduced as antiviral agents to bind with the pseudoknot and disrupt its stimulation of -1 PRF. If successfully developed, small molecule therapy that targets -1 PRF in SARS-CoV-2 is an excellent strategy to improve patients prognoses. Crucial to developing these successful therapies is modeling the structure of the SARS-CoV-2 -1 PRF pseudoknot. Following a structural alignment approach, we identify similarities in the -1 PRF pseudoknots of the novel coronavirus SARS-CoV-2, the original SARS-CoV, as well as a third coronavirus: MERS-CoV, the coronavirus responsible for Middle East Respiratory Syndrome (MERS). In addition, we provide a better understanding of the SARS-CoV-2 -1 PRF pseudoknot by comprehensively investigating the structural landscape using a hierarchical folding approach. Since understanding the impact of mutations is vital to long-term success of treatments that are based on predicted RNA functional structures, we provide insight on SARS-CoV-2 -1 PRF pseudoknot sequence mutations and their effect on the resulting structure and its function.

bioinformatics↗

bioRxiv2020

In silico prediction of COVID-19 test efficiency with DinoKnot

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus spreading across the world causing the disease COVID-19. The diagnosis of COVID-19 is done by quantitative reverse-transcription polymer chain reaction (qRT-PCR) testing which utilizes different primer-probe sets depending on the assay used. Using in silico analysis we aimed to determine how the secondary structure of the SARS-CoV-2 RNA genome affects the interaction between the reverse primer during qRT-PCR and how it relates to the experimental primer-probe test efficiencies. We introduce the program DinoKnot (Duplex Interaction of Nucleic acids with pseudoKnots) that follows the hierarchical folding hypothesis to predict the secondary structure of two interacting nucleic acid strands (DNA/RNA) of similar or different type. DinoKnot is the first program that utilizes stable stems in both strands as a guide to find the structure of their interaction. Using DinoKnot we predicted the interaction of the reverse primers used in four common COVID-19 qRT-PCR tests with the SARS-CoV-2 RNA genome. In addition, we predicted how 12 mutations in the primer/probe binding region may affect the primer/probe ability and subsequent SARS-CoV-2 detection. While we found all reverse primers are capable of interacting with their target area, we identified partial mismatching between the SARS-CoV-2 genome and some reverse primers. We predicted three mutations that may prevent primer binding, reducing the ability for SARS-CoV-2 detection. We believe our contributions can aid in the design of a more sensitive SARS-CoV-2 test. Author summaryThe current testing for the disease COVID-19 that is caused by the novel cornonavirus SARS-CoV-2 uses oligonucleotides called primers that bind to specific target regions on the SARS-CoV-2 genome to detect the virus. Our goal was to use computational tools to predict how the structure of the SARS-CoV-2 RNA genome affects the ability of the primers to bind to their target region. We introduce the program DinoKnot (Duplex interaction of nucleic acids with pseudoknots) that is able to predict the interactions between two DNA or RNA molecules. We used DinoKnot to predict the efficiency of four common COVID-19 tests, and the effect of mutations in the SARS-CoV-2 virus on ability of the COVID-19 tests in detecting those strains. We predict partial mismatching between some primers and the SARS-CoV-2 genome but that all primers are capable of interacting with their target areas. We also predict three mutations that prevent primer binding and thus SARS-CoV-2 detection. We discuss the limitations of the current COVID-19 testing and suggest the design of a more sensitive COVID-19 test that can be aided by our findings.

bioinformatics↗

bioRxiv2018

Estrogen receptor regulates hormone-induced growth arrest in a luminal A like breast cancer model

Estrogen receptor positive (ER+) breast cancer has been divided into two subtypes, luminal A and luminal B, which differ in their ER expression and response to hormone therapy. The absence of luminal A cell lines means the extensive amount of in vitro work studying the response to hormones in ER+ breast cancers is biased for the luminal B subtype. We have developed a luminal A like cell model by increasing the ER expression in the MCF-7 cell line. Our results show that increased ER expression promotes an anti-proliferative response to estrogen through regulation of genes involved in the G1/S-phase transition of the cell cycle. Furthermore, increased ER expression increases ER-DNA binding in the absence of estrogen and regulates basal gene transcription by promoting DNA looping. These results provide novel evidence that the characteristic increased ER expression of luminal A tumors may promote a novel chromatin configuration that enables growth of these tumors in the absence of estrogen and enables gene repression in the presence of hormones.

pathology↗