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Synonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological impact and may lead to disease. Existing computational methods for evaluating the effect of sSNVs suffer from the lack of gold-standard training/evaluation data and exhibit over-reliance on sequence conservation signals. We developed synVep (synonymous Variant effect predictor), a machine learning-based method that overcomes both of these limitations. Our training data was a combination of variants reported by gnomAD (observed) and those unreported, but possible in the human genome (generated). We used positive-unlabeled learning to purify the generated variant set of any likely unobservable variants. We then trained two sequential extreme gradient boosting models to identify subsets of the remaining variants putatively enriched and depleted in effect. Our method attained 90% precision/recall on a previously unseen set of variants. Furthermore, although synVep does not explicitly use conservation, its scores correlated with evolutionary distances between orthologs in cross-species variation analysis. synVep was also able to differentiate pathogenic vs. benign variants, as well as splice-site disrupting variants (SDV) vs. non-SDVs. Thus, synVep provides an important improvement in annotation of sSNVs, allowing users to focus on variants that most likely harbor effects. Availability synVep webserver for online query: https://services.bromberglab.org/synvep; For local runs Python script (https://bitbucket.org/bromberglab/synvep_local) and prediction database (https://zenodo.org/record/4763256) are also available.

The past two decades of analytical efforts have highlighted how much more remains to be learned about the human genome and, particularly, its complex involvement in promoting disease development and progression. While numerous computational tools exist for the assessment of the functional and pathogenic effects of genome variants, their precision is far from satisfactory, particularly for clinical use. Accumulating evidence also suggests that the human microbiome's interaction with the human genome plays a critical role in determining health and disease states. While numerous microbial taxonomic groups and molecular functions of the human microbiome have been associated with disease, the reproducibility of these findings is lacking. The human microbiome–genome interaction in healthy individuals is even less well understood. This review summarizes the available computational methods built to analyze the effect of variation in the human genome and microbiome. We address the applicability and precision of these methods across their possible uses. We also briefly discuss the exciting, necessary, and now possible integration of the two types of data to improve the understanding of pathogenicity mechanisms.

Metal-binding proteins have a central role in maintaining life processes. Nearly one-third of known protein structures contain metal ions that are used for a variety of needs, such as catalysis, DNA/RNA binding, protein structure stability, etc. Identifying metal-binding proteins is thus crucial for understanding the mechanisms of cellular activity. However, experimental annotation of protein metal-binding potential is severely lacking, while computational techniques are often imprecise and of limited applicability. We developed a novel machine learning-based method, mebipred, for identifying metal-binding proteins from sequence-derived features. This method is nearly 90% accurate in recognizing proteins that bind metal ions and ion containing ligands. Moreover, the identity of ten ubiquitously present metal ions and ion-containing ligands can be annotated. mebipred is reference-free, i.e. no sequence alignments are involved, and outperforms other prediction methods, both in speed and accuracy. mebipred can also identify protein metal-binding capabilities from short sequence stretches and, thus, may be useful for the annotation of metagenomic samples metal requirements inferred from translated sequencing reads. We performed an analysis of microbiome data and found that ocean, hot spring sediments and soil microbiomes use a more diverse set of metals than human host-related ones. For human-hosted microbiomes, physiological conditions explain the observed metal preferences. Similarly, subtle changes in ocean sample ion concentration affect the abundance of relevant metal-binding proteins. These results are highlight mebipreds utility in analyzing microbiome metal requirements. mebipred is available as a web server at services.bromberglab.org/mebipred and as a standalone package at https://pypi.org/project/mymetal/

Background: Aedes aegypti (L.) is an urban mosquito, vector of several arboviruses that cause severe diseases in hundreds of million people each year. The resistance to synthetic insecticides developed by Ae. aegypti populations worldwide has contributed to failures in vector control campaigns, increasing the impact of arbovirus diseases. In this context, plant-derived essential oils with larvicidal activity could be an attractive alternative for vector control. However, the mode of action and the detoxificant response of mosquitoes to plant derived compounds have not been established, impairing the optimization of their use. Methods and findings: Here we compare gene expression in Ae. aegypti larvae after 14 hrs of exposure to Eucalyptus camaldulensis essential oil with a control group exposed to vehicle (acetone) for the same lapse, by using RNA-Seq. We found differentially expressed genes encoding for cuticle proteins, fatty-acid synthesis, membrane transporters and detoxificant related gene families (i.e. heat shock proteins, cytochromes P450, glutathione transferases, UDP-glycosyltransferases and ABC transporters). Finally, our RNA-Seq and molecular docking results provide evidence pointing to a central involvement of chemosensory proteins in the detoxificant response in mosquitoes. Conclusions and significance: Our work contributes to the understanding of the physiological response of Ae. aegypti larvae to an intoxication with a natural toxic distilled from Eucalyptus leafs. The results suggest an involvement of most of the gene families associated to detoxification of xenobiotics in insects. Noteworthy, this work provides important information regarding the implication of chemosensory proteins in the detoxification of a natural larvicide. Understanding the mode of detoxification of Eucalyptus distilled compounds could contribute to their implementation as a tool in mosquito control., Los datos de la investigación están disponibles en la base de datos NCBI SRA (número de acceso Bioproject PRJNA671513) (link en "Documentos relacionados"), Centro Regional de Estudios Genómicos

Background: We study the limits imposed by transcription factor specificity on the maximum size of a genetic regulatory network. Results: Most regular expressions for natural transcription factor binding site motifs are separated in sequence space by only one to three motif-discriminating positions. This mild specificity requirement puts the number of transcription factors that can coexist with minimal crosstalk on the order of ten thousand, which would fully utilize the space of DNA subsequences. An expanded alphabet with modified bases can further raise this limit by several orders of magnitude, at the expense of sequence space usage. Conclusions: Based on this analysis, thousands of transcription factor binding site motifs may await discovery.

Linear motifs are short protein subsequences that mediate protein interactions. Hundreds of motif classes including thousands of motif instances are known. Our theory estimates how many motif classes remain undiscovered. As commonly done, we describe motif classes as regular expressions specifying motif length and the allowed amino acids at each motif position. We measure motif specificity for a pair of motif classes by quantifying how many motif-discriminating positions prevent a protein subsequence from matching the two classes at once. We derive theorems for the maximal number of motif classes that can simultaneously maintain a certain number of motif-discriminating positions between all pairs of classes in the motif universe, for a given amino acid alphabet. We also calculate the fraction of all protein subsequences that would belong to a motif class if all potential motif classes came into existence. Naturally occurring pairs of motif classes present most often a single motif-discriminating position. This mild specificity maximizes the potential number of coexisting motif classes, the expansion of the motif universe due to amino acid modifications and the fraction of amino acid sequences that code for a motif instance. As a result, thousands of linear motif classes may remain undiscovered.

The majority of microbial genomes have yet to be cultured, and most proteins predicted from microbial genomes or sequenced from the environment cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the full functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. The scientific community needs a means to capture the functionally and evolutionarily relevant features underlying biology, independent of our incomplete reference databases. Such a model can form the basis for transfer learning tasks, enabling downstream applications in environmental microbiology, medicine, and bioengineering. Here we present LookingGlass, a deep learning model capturing a “universal language of life”. LookingGlass encodes contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, distinguishing reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass is the first contextually-aware, general purpose pre-trained “biological language” representation model for short-read DNA sequences. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth.AvailabilityThe pretrained LookingGlass model and the transfer learning-derived models demonstrated in this paper are available in the LookingGlass release v1.01. The open source fastBio Github repository and python package provides classes and functions for training and fine tuning deep learning models with biological data2. Code for reproducing analyses presented in this paper are available as an open source Github repository3.

Root hair polar growth is endogenously controlled by auxin and sustained by oscillating levels of reactive oxygen species (ROS). These cells extend several hundred-fold their original size toward signals important for plant survival. Although their final cell size is of fundamental importance, the molecular mechanisms that control it remain largely unknown. Here we show that ROS production is controlled by the transcription factor RSL4, which in turn is transcriptionally regulated by auxin through several auxin response factors (ARFs). In this manner, auxin controls ROS-mediated polar growth by activating RSL4, which then up-regulates the expression of genes encoding NADPH oxidases (also known as RESPIRATORY BURST OXIDASE HOMOLOG proteins) and class III peroxidases, which catalyze ROS production. Chemical or genetic interference with ROS balance or peroxidase activity affects root hair final cell size. Overall, our findings establish amolecular link between auxin and ROS-mediated polar root hair growth. Fil: Mangano, Silvina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina Fil: Denita Juárez, Silvina Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina Fil: Choi, Hee-Seung. Seoul National University; Corea del Sur Fil: Marzol, Eliana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina Fil: Hwang, Youra. Seoul National University; Corea del Sur Fil: Ranocha, Philippe. Universite de Toulose - Le Mirail; Francia Fil: Velásquez, Silvia Melina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina Fil: Borassi, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina Fil: Barberini, María Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; Argentina Fil: Aptekmann, Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina Fil: Muschietti, Jorge Prometeo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Biodiversidad y Biología Experimental; Argentina Fil: Nadra, Alejandro Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina Fil: Dunand, Christophe. Universite de Toulose - Le Mirail; Francia Fil: Cho, Hyung-Taeg. Seoul National University; Corea del Sur Fil: Estevez, Jose Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina

The subtle mechanisms by which protein-DNA interactions remain functional across a wide range of temperatures are largely unknown. In this work, we manually curated available information relating fully sequenced archaeal genomes with organism growth temperatures. We built a motif that represents the core promoter of each species and calculated its information content. We then studied the relation between optimal growth temperature (OGT) and information content (IC) in the promoter region.We found a positive correlation between G + C content and OGT in tRNA regions and not in overall genome. Furthermore, we found that there is a positive correlation between information content and optimal growth temperatures in Archaea. This can't be explained by an increased C+G composition nor by other obvious mechanisms. These findings suggest that increased information content could produce a positive fitness in organisms living at high temperatures. We suggest that molecular information theory may need to be adapted for hyperthermophiles. Fil: Aptekmann, Ariel. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina Fil: Nadra, Alejandro Daniel. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina

Root hair polar growth is endogenously controlled by auxin and sustained by oscillating levels of reactive oxygen species (ROS). These cells extend several hundred-fold their original size toward signals important for plant survival. Although their final cell size is of fundamental importance, the molecular mechanisms that control it remain largely unknown. Here, we show that ROS production is controlled by the transcription factors RSL4, which in turn is transcriptionally regulated by auxin through several Auxin Responsive Factors (ARFs). In this manner, auxin controls ROS-mediated polar growth by activating RSL4, which then upregulates the expression of genes encoding NADPH oxidases (also known as RBOHs, RESPIRATORY BURST OXIDASE HOMOLOG proteins) and Class-III Peroxidases (PER), which catalyse ROS production. Chemical or genetic interference with the ROS balance or peroxidase activity affect root hair final cell size. Overall, our findings establish a molecular link between auxin regulated ARFs-RSL4 and ROS-mediated polar root hair growth.Significance StatementTip-growing root hairs are excellent model systems to decipher the molecular mechanism underlying reactive oxygen species (ROS)-mediated cell elongation. Root hairs are able to expand in response to external signals, increasing several hundred-fold their original size, which is important for survival of the plant. Although their final cell size is of fundamental importance, the molecular mechanisms that control it remain largely unknown. In this study, we propose a molecular mechanism that links the auxin-Auxin Response Factors (ARFs) module to activation of RSL4, which directly targets genes encoding ROS-producing enzymes, such as NADPH oxidases (or RBOHs) and secreted type-III peroxidases (PERs). Activation of these genes impacts apoplastic ROS homeostasis, thereby stimulating root hair cell elongation.