Simplifications and approximations in a single-gene circuit modeling.

The absence of detailed knowledge about regulatory interactions makes the use of phenomenological assumptions mandatory in cell biology modeling. Furthermore, the challenges associated with the analysis of these models compel the implementation of mathematical approximations. However, the constraints these methods introduce to biological interpretation are sometimes neglected. Consequently, understanding these restrictions is a very important task for systems biology modeling. In this article, we examine the impact of such simplifications, taking the case of a single-gene autoinhibitory circuit; however, our conclusions are not limited solely to this instance. We demonstrate that models grounded in the same biological assumptions but described at varying levels of detail can lead to different outcomes, that is, different and contradictory phenotypes or behaviors. Indeed, incorporating specific molecular processes like translation and elongation into the model can introduce instabilities and oscillations not seen when these processes are assumed to be instantaneous. Furthermore, incorporating a detailed description of promoter dynamics, usually described by a phenomenological regulatory function, can lead to instability, depending on the cooperative binding mechanism that is acting. Consequently, although the use of a regulating function facilitates model analysis, it may mask relevant aspects of the system's behavior. In particular, we observe that the two cooperative binding mechanisms, both compatible with the same sigmoidal function, can lead to different phenotypes, such as transcriptional oscillations with different oscillation frequencies.

Transcriptomic landscape of the interaction between the entomopathogenic fungus Beauveria bassiana and its tolerant host Tribolium castaneum revealed by dual RNA-seq.

Entomopathogenic fungi such as Beauveria bassiana are the only insect pathogens able to start the infection process by penetrating through the host cuticle. However, some insects try to avoid fungal infection by embedding their cuticle with antifungal compounds. This is the case of the red flour beetle Tribolium castaneum, which generates economical loss of great significance in stored product environments worldwide. In this study, T. castaneum adults were fed during different time periods (from 3 to 72 h) on B. bassiana conidia-covered corn kernels. The progression of fungal infection was monitored using the dual RNA-seq technique to reconstruct the temporal transcriptomic profile and to perform gene enrichment analyses in both interacting organisms. After mapping the total reads with the B. bassiana genome, 904 genes were identified during this process. The more expressed fungal genes were related to carbon catabolite repression, cation binding, peptidase inhibition, redox processes, and stress response. Several immune-related genes from Toll, IMD, and JNK pathways, as well as genes related to chitin modification, were found to be differentially expressed in fungus-exposed T. castaneum. This study represents the first dual transcriptomic approach to help understand the interaction between the entomopathogenic fungus B. bassiana and its tolerant host T. castaneum.

Dicodon-based measures for modeling gene expression.

MOTIVATION: Codon usage preference patterns have been associated with modulation of translation efficiency, protein folding, and mRNA decay. However, new studies support that codon pair usage has also a remarkable effect at the gene expression level. Here, we expand the concept of CAI to answer if codon pair usage patterns can be understood in terms of codon usage bias, or if they offer new information regarding coding translation efficiency. RESULTS: Through the implementation of a weighting strategy to consider the dicodon contributions, we observe that the dicodon-based measure has greater correlations with gene expression level than CAI. Interestingly, we have noted that dicodons associated with a low value of adaptiveness are related to dicodons which mediate strong translational inhibition in yeast. We have also noticed that some codon-pairs have a smaller dicodon contribution than estimated by the product of the respective codon contributions. AVAILABILITY AND IMPLEMENTATION: Scripts, implemented in Python, are freely available for download at https://zenodo.org/record/7738276#.ZBIDBtLMIdU.

Cell annotation using scRNA-seq data: A protein-protein interaction network approach.

Pathway analysis is an important step in the interpretation of single cell transcriptomic data, as it provides powerful information to detect which cellular processes are active in each individual cell. We have recently developed a protein-protein interaction network-based framework to quantify pluripotency associated pathways from scRNA-seq data. On this occasion, we extend this approach to quantify the activity of a pathway associated with any biological process, or even any list of genes. A systems-level characterization of pathway activities across multiple cell types provides a broadly applicable tool for the analysis of pathways in both healthy and disease conditions. Dysregulated cellular functions are a hallmark of a wide spectrum of human disorders, including cancer and autoimmune diseases. Here, we illustrate our method by analyzing various biological processes in healthy and cancer breast samples. Using this approach we found that tumor breast cells, even when they form a single group in the UMAP space, keep diverse biological programs active in a differentiated manner within the cluster.•We implement a protein-protein interaction network-based approach to quantify the activity of different biological processes.•The methodology can be used for cell annotation in scRNA-seq studies and is freely available as R package.

Analysis of the Interactome of the Toxoplasma gondii Tgj1 HSP40 Chaperone.

Toxoplasma gondii is an obligate intracellular apicomplexan that causes toxoplasmosis in humans and animals. Central to its dissemination and pathogenicity is the ability to rapidly divide in the tachyzoite stage and infect any type of nucleated cell. Adaptation to different cell contexts requires high plasticity in which heat shock proteins (Hsps) could play a fundamental role. Tgj1 is a type I Hsp40 of T. gondii, an ortholog of the DNAJA1 group, which is essential during the tachyzoite lytic cycle. Tgj1 consists of a J-domain, ZFD, and DNAJ_C domains with a CRQQ C-terminal motif, which is usually prone to lipidation. Tgj1 presented a mostly cytosolic subcellular localization overlapping partially with endoplasmic reticulum. Protein-protein Interaction (PPI) analysis showed that Tgj1 could be implicated in various biological pathways, mainly translation, protein folding, energy metabolism, membrane transport and protein translocation, invasion/pathogenesis, cell signaling, chromatin and transcription regulation, and cell redox homeostasis among others. The combination of Tgj1 and Hsp90 PPIs retrieved only 70 interactors linked to the Tgj1-Hsp90 axis, suggesting that Tgj1 would present specific functions in addition to those of the Hsp70/Hsp90 cycle, standing out invasion/pathogenesis, cell shape motility, and energy pathway. Within the Hsp70/Hsp90 cycle, translation-associated pathways, cell redox homeostasis, and protein folding were highly enriched in the Tgj1-Hsp90 axis. In conclusion, Tgj1 would interact with a wide range of proteins from different biological pathways, which could suggest a relevant role in them.

Single cell gene expression profiling of nasal ciliated cells reveals distinctive biological processes related to epigenetic mechanisms in patients with severe COVID-19.

OBJECTIVE: To explore the molecular processes associated with cellular regulatory programs in patients with COVID-19, including gene activation or repression mediated by epigenetic mechanisms. We hypothesized that a comprehensive gene expression profiling of nasopharyngeal epithelial cells might expand our understanding of the pathogenic mechanisms of severe COVID-19. METHODS: We used single-cell RNA sequencing (scRNAseq) profiling of ciliated cells (n = 12,725) from healthy controls (SARS-CoV-2 negative n = 13) and patients with mild/moderate (n = 13) and severe (n = 14) COVID-19. ScRNAseq data at the patient level were used to perform gene set and pathway enrichment analyses. We prioritized candidate miRNA-target interactions and epigenetic mechanisms. RESULTS: We found that mild/moderate COVID-19 compared to healthy controls had upregulation of gene expression signatures associated with mitochondrial function, misfolded proteins, and membrane permeability. In addition, we found that compared to mild/moderate disease, severe COVID-19 had downregulation of epigenetic mechanisms, including DNA and histone H3K4 methylation and chromatin remodelling regulation. Furthermore, we found 11-ranked miRNAs that may explain miRNA-dependent regulation of histone methylation, some of which share seed sequences with SARS-CoV-2 miRNAs. CONCLUSION: Our results may provide novel insights into the epigenetic mechanisms mediating the clinical course of SARS-CoV-2 infection.

ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data.

Trajectory inference is a common application of scRNA-seq data. However, it is often necessary to previously determine the origin of the trajectories, the stem or progenitor cells. In this work, we propose a computational tool to quantify pluripotency from single cell transcriptomics data. This approach uses the protein-protein interaction (PPI) network associated with the differentiation process as a scaffold and the gene expression matrix to calculate a score that we call differentiation activity. This score reflects how active the differentiation network is in each cell. We benchmark the performance of our algorithm with two previously published tools, LandSCENT (Chen et al., 2019) and CytoTRACE (Gulati et al., 2020), for four healthy human data sets: breast, colon, hematopoietic and lung. We show that our algorithm is more efficient than LandSCENT and requires less RAM memory than the other programs. We also illustrate a complete workflow from the count matrix to trajectory inference using the breast data set.•ORIGINS is a methodology to quantify pluripotency from scRNA-seq data implemented as a freely available R package.•ORIGINS uses the protein-protein interaction network associated with differentiation and the data set expression matrix to calculate a score (differentiation activity) that quantifies pluripotency for each cell.

In-depth comparative analysis of Tritrichomonas foetus transcriptomics reveals novel genes linked with adaptation to feline host.

Tritrichomonas foetus is a flagellated parasite able to infect cattle, cats, and pigs. Despite its prevalence, feline tritrichomonosis has received markedly less attention than venereal infection, and little information about the molecular mechanisms that participate in feline host infection is available. Through a bioinformatics approach, we integrated public transcriptomic data for three T. foetus isolates and explored the differences at transcript level with a focus on pathogenesis and adaptation processes, particularly for the feline isolate. Our analysis revealed higher abundance levels of predicted virulence factors, such as proteases and surface antigens. Additionally, by a comparative and expression analysis of T. foetus genes, we proposed putative virulence factors that could be involved in feline infection. Finally, we identified a great proportion of predicted transcription factors of the MYB protein family and, by a promoter analysis, we revealed that MYB-related proteins could participate in the regulation of gene transcription in T. foetus. In conclusion, this integrated approach is a valuable resource for future studies of host-pathogen interactions and identifying new gene targets for improved feline tritrichomonosis diagnosis and treatment.

A new analogue of islet neogenesis associated protein with higher structural and plasma stability.

Islet Neogenesis Associated Protein pentadecapeptide (INGAP-PP) increases ß-cell mass and function in experimental animals. A short clinical trial also yielded promising results. However, HTD4010, a new peptide derived from INGAP-PP, was developed in order to optimize its specific effects by minimizing its side effects. To study and compare the tertiary structure, stability dynamics, and plasma stability of HTD4010, an INGAP-PP analogue. Both peptides were pre-incubated in human, rat and mouse plasma at 37 °C, and their presence was identified and quantified by high performance liquid chromatography at different time-points. GROMACS 2019 package and the Gromos 54A7 force field were used to evaluate overall correlated motion of the peptide molecule during molecular dynamics simulation by essential dynamics. HTD4010 exhibited significantly larger plasma stability than INGAP-PP, and its structural stability was almost 3.36-fold higher than INGAP-PP. These results suggest that HTD4010 may facilitate longer tissue interaction, thereby developing higher potential biological effects. If so, HTD4010 may become a promising therapeutic agent to treat people with diabetes. Communicated by Ramaswamy H. Sarma.

SARS-CoV-2 Codon Usage Bias Downregulates Host Expressed Genes With Similar Codon Usage.

Severe acute respiratory syndrome has spread quickly throughout the world and was declared a pandemic by the World Health Organization (WHO). The pathogenic agent is a new coronavirus (SARS-CoV-2) that infects pulmonary cells with great effectiveness. In this study we focus on the codon composition for the viral protein synthesis and its relationship with the protein synthesis of the host. Our analysis reveals that SARS-CoV-2 preferred codons have poor representation of G or C nucleotides in the third position, a characteristic which could result in an unbalance in the tRNAs pools of the infected cells with serious implications in host protein synthesis. By integrating this observation with proteomic data from infected cells, we observe a reduced translation rate of host proteins associated with highly expressed genes and that they share the codon usage bias of the virus. The functional analysis of these genes suggests that this mechanism of epistasis can contribute to understanding how this virus evades the immune response and the etiology of some deleterious collateral effect as a result of the viral replication. In this manner, our finding contributes to the understanding of the SARS-CoV-2 pathogeny and could be useful for the design of a vaccine based on the live attenuated strategy.

Alternating regimes of motion in a model with cell-cell interactions.

Cellular movement is a complex dynamic process, resulting from the interaction of multiple elements at the intra- and extracellular levels. This epiphenomenon presents a variety of behaviors, which can include normal and anomalous diffusion or collective migration. In some cases, cells can get neighborhood information through chemical or mechanical cues. A unified understanding about how such information can influence the dynamics of cell movement is still lacking. In order to improve our comprehension of cell migration we have considered a cellular Potts model where cells move actively in the direction of a driving field. The intensity of this driving field is constant, while its orientation can evolve according to two alternative dynamics based on the Ornstein-Uhlenbeck process. In one case, the next orientation of the driving field depends on the previous direction of the field. In the other case, the direction update considers the mean orientation performed by the cell in previous steps. Thus, the latter update rule mimics the ability of cells to perceive the environment, avoiding obstacles and thus increasing the cellular displacement. Different cell densities are considered to reveal the effect of cell-cell interactions. Our results indicate that both dynamics introduce temporal and spatial correlations in cell velocity in a friction-coefficient and cell-density-dependent manner. Furthermore, we observe alternating regimes in the mean-square displacement, with normal and anomalous diffusion. The crossovers between diffusive and directed motion regimes are strongly affected by both the driving field dynamics and cell-cell interactions. In this sense, when cell polarization update grants information about the previous cellular displacement, the duration of the diffusive regime decreases, particularly in high-density cultures.

Synonymous variants in holoprosencephaly alter codon usage and impact the Sonic Hedgehog protein.

Synonymous single nucleotide variants (sSNVs) have been implicated in various genetic disorders through alterations of pre-mRNA splicing, mRNA structure and miRNA regulation. However, their impact on synonymous codon usage and protein translation remains to be elucidated in clinical context. Here, we explore the functional impact of sSNVs in the Sonic Hedgehog (SHH) gene, identified in patients affected by holoprosencephaly, a congenital brain defect resulting from incomplete forebrain cleavage. We identified eight sSNVs in SHH, selectively enriched in holoprosencephaly patients as compared to healthy individuals, and systematically assessed their effect at both transcriptional and translational levels using a series of in silico and in vitro approaches. Although no evidence of impact of these sSNVs on splicing, mRNA structure or miRNA regulation was found, five sSNVs introduced significant changes in codon usage and were predicted to impact protein translation. Cell assays demonstrated that these five sSNVs are associated with a significantly reduced amount of the resulting protein, ranging from 5% to 23%. Inhibition of the proteasome rescued the protein levels for four out of five sSNVs, confirming their impact on protein stability and folding. Remarkably, we found a significant correlation between experimental values of protein reduction and computational measures of codon usage, indicating the relevance of in silico models in predicting the impact of sSNVs on translation. Considering the critical role of SHH in brain development, our findings highlight the clinical relevance of sSNVs in holoprosencephaly and underline the importance of investigating their impact on translation in human pathologies.

Mesorhizobium intechi sp. nov. isolated from nodules of Lotus tenuis in soils of the Flooding Pampa, Argentina.

Three symbiotic nitrogen-fixing bacteria (BD68T, BD66 and BD73) isolated from root nodules of Lotus tenuis in lowland soils of the Flooding Pampa (Argentina), previously classified as members of the Mesorhizobium genus, were characterized in this study. Phylogenetic analysis of their 16S rRNA gene sequences showed a close relationship to M. japonicum MAFF 303099T, M. erdmanii USDA 3471T, M. carmichaelinearum ICMP 18942T, M. opportunistum WSM 2975T and M. jarvisii ATCC 33699T, with sequence identities of 99.72%-100%. Multilocus sequence analysis of other housekeeping genes revealed that the three isolates belonged to a phylogenetically distinct clade within the genus Mesorhizobium. Strain BD68T was designated as the group representative and its genome was fully sequenced. The average nucleotide identity and in silico DNA-DNA hybridization comparisons between BD68T and the most related type strains showed values below the accepted threshold for species discrimination. Phenotypic and chemotaxonomic features were also studied. Based on these results, BD68T, BD66 and BD73 could be considered to represent a novel species of the genus Mesorhizobium, for which the name Mesorhizobium intechi sp. nov. is hereby proposed. The type strain of this species is BD68T (=CECT 9304T=LMG 30179T).

Gene target discovery with network analysis in Toxoplasma gondii.

Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogen biology. In this paper, we apply a gene regulatory network approach to analyze transcriptomic data of the parasite Toxoplasma gondii. By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of T. gondii were embedded into the dynamics of a gene regulatory network. Thus, through this methodology we were able to reconstruct a gene regulatory network able to emulate the life cycle of the pathogen. The community network analysis has revealed that nodes of the network can be organized in seven communities which allow us to assign putative functions to 338 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identified a small gene circuit that drives a series of phenotypic transitions that characterize the life cycle of this pathogen. These new findings can contribute to the understanding of parasite pathogenesis.

Prediction of cell position using single-cell transcriptomic data: an iterative procedure.

Single-cell sequencing reveals cellular heterogeneity but not cell localization. However, by combining single-cell transcriptomic data with a reference atlas of a small set of genes, it would be possible to predict the position of individual cells and reconstruct the spatial expression profile of thousands of genes reported in the single-cell study. With the purpose of developing new algorithms, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium organized a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC). Within this context, we describe here our proposed procedures for adequate reference genes selection, and an iterative procedure to predict spatial expression profile of other genes.

Phylogenomics of tomato chloroplasts using assembly and alignment-free method.

Phylogenetics and population genetics are central disciplines in evolutionary biology. Both are based on the comparison of single DNA sequences, or a concatenation of a number of these. However, with the advent of next-generation DNA sequencing technologies, the approaches that consider large genomic data sets are of growing importance for the elucidation of evolutionary relationships among species. Among these approaches, the assembly and alignment-free methods which allow an efficient distance computation and phylogeny reconstruction are of great importance. However, it is not yet clear under what quality conditions and abundance of genomic data such methods are able to infer phylogenies accurately. In the present study we assess the method originally proposed by Fan et al. for whole genome data, in the elucidation of Tomatoes' chloroplast phylogenetics using short read sequences. We find that this assembly and alignment-free method is capable of reproducing previous results under conditions of high coverage, given that low frequency k-mers (i.e. error prone data) are effectively filtered out. Finally, we present a complete chloroplast phylogeny for the best data quality candidates of the recently published 360 tomato genomes.

Differential bicodon usage in lowly and highly abundant proteins.

Degeneracy in the genetic code implies that different codons can encode the same amino acid. Usage preference of synonymous codons has been observed in all domains of life. There is much evidence suggesting that this bias has a major role on protein elongation rate, contributing to differential expression and to co-translational folding. In addition to codon usage bias, other preference variations have been observed such as codon pairs. In this paper, I report that codon pairs have significant different frequency usage for coding either lowly or highly abundant proteins. These usage preferences cannot be explained by the frequency usage of the single codons. The statistical analysis of coding sequences of nine organisms reveals that in many cases bicodon preferences are shared between related organisms. Furthermore, it is observed that misfolding in the drug-transport protein, encoded by MDR1 gene, is better explained by a big change in the pause propensity due to the synonymous bicodon variant, rather than by a relatively small change in codon usage. These findings suggest that codon pair usage can be a more powerful framework to understand translation elongation rate, protein folding efficiency, and to improve protocols to optimize heterologous gene expression.

Bicodon bias can determine the role of synonymous SNPs in human diseases.

BACKGROUND: For a long time synonymous single nucleotide polymorphisms were considered as silent mutations. However, nowadays it is well known that they can affect protein conformation and function, leading to altered disease susceptibilities, differential prognosis and/or drug responses, among other clinically relevant genetic traits. This occurs through different mechanisms: by disrupting the splicing signals of precursor mRNAs, affecting regulatory binding-sites of transcription factors and miRNAs, or by modifying the secondary structure of mRNAs. RESULTS: In this paper we considered 22 human genetic diseases or traits, linked to 35 synonymous single nucleotide polymorphisms in 27 different genes. We performed a local sequence context analysis in terms of the ribosomal pause propensity affected by synonymous single nucleotide polymorphisms. We found that synonymous mutations related to the above mentioned mechanisms presented small pause propensity changes, whereas synonymous mutations that were not related to those mechanisms presented large pause propensity changes. On the other hand, we did not observe large variations in the codon usage of codons associated with these mutations. Furthermore, we showed that the changes in the pause propensity associated with benign sSNPs are significantly lower than the pause propensity changes related to sSNPs associated to diseases. CONCLUSIONS: These results suggest that the genetic diseases or traits related to synonymous mutations with large pause propensity changes, could be the consequence of another mechanism underlying non-silent synonymous mutations. Namely, alternative protein configuration related, in turn, to alterations in the ribosome-mediated translational attenuation program encoded by pairs of consecutive codons, not codons. These findings shed light on the latter mechanism based on the perturbation of the co-translational folding process.

Modeling the flux of metabolites in the juvenile hormone biosynthesis pathway using generalized additive models and ordinary differential equations.

Juvenile hormone (JH) regulates development and reproductive maturation in insects. The corpora allata (CA) from female adult mosquitoes synthesize fluctuating levels of JH, which have been linked to the ovarian development and are influenced by nutritional signals. The rate of JH biosynthesis is controlled by the rate of flux of isoprenoids in the pathway, which is the outcome of a complex interplay of changes in precursor pools and enzyme levels. A comprehensive study of the changes in enzymatic activities and precursor pool sizes have been previously reported for the mosquito Aedes aegypti JH biosynthesis pathway. In the present studies, we used two different quantitative approaches to describe and predict how changes in the individual metabolic reactions in the pathway affect JH synthesis. First, we constructed generalized additive models (GAMs) that described the association between changes in specific metabolite concentrations with changes in enzymatic activities and substrate concentrations. Changes in substrate concentrations explained 50% or more of the model deviances in 7 of the 13 metabolic steps analyzed. Addition of information on enzymatic activities almost always improved the fitness of GAMs built solely based on substrate concentrations. GAMs were validated using experimental data that were not included when the model was built. In addition, a system of ordinary differential equations (ODE) was developed to describe the instantaneous changes in metabolites as a function of the levels of enzymatic catalytic activities. The results demonstrated the ability of the models to predict changes in the flux of metabolites in the JH pathway, and can be used in the future to design and validate experimental manipulations of JH synthesis.