Diverse virus-encoded CRISPR-Cas systems include streamlined genome editors.

CRISPR-Cas systems are host-encoded pathways that protect microbes from viral infection using an adaptive RNA-guided mechanism. Using genome-resolved metagenomics, we find that CRISPR systems are also encoded in diverse bacteriophages, where they occur as divergent and hypercompact anti-viral systems. Bacteriophage-encoded CRISPR systems belong to all six known CRISPR-Cas types, though some lack crucial components, suggesting alternate functional roles or host complementation. We describe multiple new Cas9-like proteins and 44 families related to type V CRISPR-Cas systems, including the Casλ RNA-guided nuclease family. Among the most divergent of the new enzymes identified, Casλ recognizes double-stranded DNA using a uniquely structured CRISPR RNA (crRNA). The Casλ-RNA-DNA structure determined by cryoelectron microscopy reveals a compact bilobed architecture capable of inducing genome editing in mammalian, Arabidopsis, and hexaploid wheat cells. These findings reveal a new source of CRISPR-Cas enzymes in phages and highlight their value as genome editors in plant and human cells.

PAM-Dependent Target DNA Recognition and Cleavage by C2c1 CRISPR-Cas Endonuclease.

C2c1 is a newly identified guide RNA-mediated type V-B CRISPR-Cas endonuclease that site-specifically targets and cleaves both strands of target DNA. We have determined crystal structures of Alicyclobacillus acidoterrestris C2c1 (AacC2c1) bound to sgRNA as a binary complex and to target DNAs as ternary complexes, thereby capturing catalytically competent conformations of AacC2c1 with both target and non-target DNA strands independently positioned within a single RuvC catalytic pocket. Moreover, C2c1-mediated cleavage results in a staggered seven-nucleotide break of target DNA. crRNA adopts a pre-ordered five-nucleotide A-form seed sequence in the binary complex, with release of an inserted tryptophan, facilitating zippering up of 20-bp guide RNA:target DNA heteroduplex on ternary complex formation. Notably, the PAM-interacting cleft adopts a "locked" conformation on ternary complex formation. Structural comparison of C2c1 ternary complexes with their Cas9 and Cpf1 counterparts highlights the diverse mechanisms adopted by these distinct CRISPR-Cas systems, thereby broadening and enhancing their applicability as genome editing tools.

Temporal control of RNAi reveals both robust and labile feedback loops in the segmentation clock of the red flour beetle.

Animals from all major clades have evolved a segmented trunk, reflected in the human spine or the insect segments. These units emerge during embryogenesis from a posterior segment addition zone (SAZ), where repetitive gene activity is regulated by a mechanism described by the clock and wavefront/speed gradient model. In the red flour beetle Tribolium castaneum, RNA interference (RNAi) has been used to continuously knock down the function of primary pair-rule genes (pPRGs), caudal or Wnt pathway components, which has led to the complete breakdown of segmentation. However, it has remained untested, if this breakdown was reversible by bringing the missing gene function back to the system. To fill this gap, we established a transgenic system in T. castaneum, which allows blocking an ongoing RNAi effect with temporal control by expressing a viral inhibitor of RNAi via heat shock. We show that the T. castaneum segmentation machinery was able to reestablish after RNAi targeting the pPRGs Tc-eve, Tc-odd, and Tc-runt was blocked. However, we observed no rescue after blocking RNAi targeting Wnt pathway components. We conclude that the insect segmentation system contains both robust feedback loops that can reestablish and labile feedback loops that break down irreversibly. This combination may reconcile conflicting needs of the system: Labile systems controlling initiation and maintenance of the SAZ ensure that only one SAZ is formed. Robust feedback loops confer developmental robustness toward external disturbances.

The driving force behind tool-stone selection in the African Middle Stone Age.

In the Stone Age, the collection of specific rocks was the first step in tool making. Very little is known about the choices made during tool-stone acquisition. Were choices governed by the knowledge of, and need for, specific properties of stones? Or were the collected raw materials a mere by-product of the way people moved through the landscape? We investigate these questions in the Middle Stone Age (MSA) of southern Africa, analyzing the mechanical properties of tool-stones used at the site Diepkloof Rock Shelter. To understand knapping quality, we measure flaking predictability and introduce a physical model that allows calculating the relative force necessary to produce flakes from different rocks. To evaluate their quality as finished tools, we investigate their resistance during repeated use activities (scraping or cutting) and their strength during projectile impacts. Our findings explain tool-stone selection in two emblematic periods of the MSA, the Still Bay and Howiesons Poort, as being the result of a deep understanding of these mechanical properties. In both cases, people chose those rocks, among many others, that allowed the most advantageous trade-off between anticipated properties of finished tools and the ease of acquiring rocks and producing tools. The implications are an understanding of African MSA toolmakers as engineers who carefully weighed their choices taking into account workability and the quality of the tools they made.

Impact of the inaccessible genome on genotype imputation and genome-wide association studies.

Genotype imputation is widely used in genome-wide association studies (GWAS). However, both the genotyping chips and imputation reference panels are dependent on next-generation sequencing (NGS). Due to the nature of NGS, some regions of the genome are inaccessible to sequencing. To date, there has been no complete evaluation of these regions and their impact on the identification of associations in GWAS remains unclear. In this study, we systematically assess the extent to which variants in inaccessible regions are underrepresented on genotyping chips and imputation reference panels, in GWAS results and in variant databases. We also determine the proportion of genes located in inaccessible regions and compare the results across variant masks defined by the 1000 Genomes Project and the TOPMed program. Overall, fewer variants were observed in inaccessible regions in all categories analyzed. Depending on the mask used and normalized for region size, only 4%-17% of the genotyped variants are located in inaccessible regions and 52 to 581 genes were almost completely inaccessible. From the Cooperative Health Research in South Tyrol (CHRIS) study, we present a case study of an association located in an inaccessible region that is driven by genotyped variants and cannot be reproduced by imputation in GRCh37. We conclude that genotyping, NGS, genotype imputation and downstream analyses such as GWAS and fine mapping are systematically biased in inaccessible regions, due to missed variants and spurious associations. To help researchers assess gene and variant accessibility, we provide an online application (https://gab.gm.eurac.edu).

Identification of a robust DNA methylation signature for Fanconi anemia.

Fanconi anemia (FA) is a clinically variable and genetically heterogeneous cancer-predisposing disorder representing the most common bone marrow failure syndrome. It is caused by inactivating predominantly biallelic mutations involving >20 genes encoding proteins with roles in the FA/BRCA DNA repair pathway. Molecular diagnosis of FA is challenging due to the wide spectrum of the contributing gene mutations and structural rearrangements. The assessment of chromosomal fragility after exposure to DNA cross-linking agents is generally required to definitively confirm diagnosis. We assessed peripheral blood genome-wide DNA methylation (DNAm) profiles in 25 subjects with molecularly confirmed clinical diagnosis of FA (FANCA complementation group) using Illumina's Infinium EPIC array. We identified 82 differentially methylated CpG sites that allow to distinguish subjects with FA from healthy individuals and subjects with other genetic disorders, defining an FA-specific DNAm signature. The episignature was validated using a second cohort of subjects with FA involving different complementation groups, documenting broader genetic sensitivity and demonstrating its specificity using the EpiSign Knowledge Database. The episignature properly classified DNA samples obtained from bone marrow aspirates, demonstrating robustness. Using the selected probes, we trained a machine-learning model able to classify EPIC DNAm profiles in molecularly unsolved cases. Finally, we show that the generated episignature includes CpG sites that do not undergo functional selective pressure, allowing diagnosis of FA in individuals with reverted phenotype due to gene conversion. These findings provide a tool to accelerate diagnostic testing in FA and broaden the clinical utility of DNAm profiling in the diagnostic setting.

betAS: intuitive analysis and visualization of differential alternative splicing using beta distributions.

Next-generation RNA sequencing allows alternative splicing (AS) quantification with unprecedented resolution, with the relative inclusion of an alternative sequence in transcripts being commonly quantified by the proportion of reads supporting it as percent spliced-in (PSI). However, PSI values do not incorporate information about precision, proportional to the respective AS events' read coverage. Beta distributions are suitable to quantify inclusion levels of alternative sequences, using reads supporting their inclusion and exclusion as surrogates for the two distribution shape parameters. Each such beta distribution has the PSI as its mean value and is narrower when the read coverage is higher, facilitating the interpretability of its precision when plotted. We herein introduce a computational pipeline, based on beta distributions accurately modeling PSI values and their precision, to quantitatively and visually compare AS between groups of samples. Our methodology includes a differential splicing significance metric that compromises the magnitude of intergroup differences, the estimation uncertainty in individual samples, and the intragroup variability, being therefore suitable for multiple-group comparisons. To make our approach accessible and clear to both noncomputational and computational biologists, we developed betAS, an interactive web app and user-friendly R package for visual and intuitive differential splicing analysis from read count data.

Computational deconvolution of DNA methylation data from mixed DNA samples.

In this review, we provide a comprehensive overview of the different computational tools that have been published for the deconvolution of bulk DNA methylation (DNAm) data. Here, deconvolution refers to the estimation of cell-type proportions that constitute a mixed sample. The paper reviews and compares 25 deconvolution methods (supervised, unsupervised or hybrid) developed between 2012 and 2023 and compares the strengths and limitations of each approach. Moreover, in this study, we describe the impact of the platform used for the generation of methylation data (including microarrays and sequencing), the applied data pre-processing steps and the used reference dataset on the deconvolution performance. Next to reference-based methods, we also examine methods that require only partial reference datasets or require no reference set at all. In this review, we provide guidelines for the use of specific methods dependent on the DNA methylation data type and data availability.

CARTAR: a comprehensive web tool for identifying potential targets in chimeric antigen receptor therapies using TCGA and GTEx data.

Chimeric antigen receptor (CAR) therapy has emerged as a ground-breaking advancement in cancer treatment, harnessing the power of engineered human immune cells to target and eliminate cancer cells. The escalating interest and investment in CAR therapy in recent years emphasize its profound significance in clinical research, positioning it as a rapidly expanding frontier in the field of personalized cancer therapies. A crucial step in CAR therapy design is choosing the right target as it determines the therapy's effectiveness, safety and specificity against cancer cells, while sparing healthy tissues. Herein, we propose a suite of tools for the identification and analysis of potential CAR targets leveraging expression data from The Cancer Genome Atlas and Genotype-Tissue Expression Project, which are implemented in CARTAR website. These tools focus on pinpointing tumor-associated antigens, ensuring target selectivity and assessing specificity to avoid off-tumor toxicities and can be used to rationally designing dual CARs. In addition, candidate target expression can be explored in cancer cell lines using the expression data for the Cancer Cell Line Encyclopedia. To our best knowledge, CARTAR is the first website dedicated to the systematic search of suitable candidate targets for CAR therapy. CARTAR is publicly accessible at https://gmxenomica.github.io/CARTAR/.

Optimizing in silico drug discovery: simulation of connected differential expression signatures and applications to benchmarking.

BACKGROUND: We present a novel simulation method for generating connected differential expression signatures. Traditional methods have struggled with the lack of reliable benchmarking data and biases in drug-disease pair labeling, limiting the rigorous benchmarking of connectivity-based approaches. OBJECTIVE: Our aim is to develop a simulation method based on a statistical framework that allows for adjustable levels of parametrization, especially the connectivity, to generate a pair of interconnected differential signatures. This could help to address the issue of benchmarking data availability for connectivity-based drug repurposing approaches. METHODS: We first detailed the simulation process and how it reflected real biological variability and the interconnectedness of gene expression signatures. Then, we generated several datasets to enable the evaluation of different existing algorithms that compare differential expression signatures, providing insights into their performance and limitations. RESULTS: Our findings demonstrate the ability of our simulation to produce realistic data, as evidenced by correlation analyses and the log2 fold-change distribution of deregulated genes. Benchmarking reveals that methods like extreme cosine similarity and Pearson correlation outperform others in identifying connected signatures. CONCLUSION: Overall, our method provides a reliable tool for simulating differential expression signatures. The data simulated by our tool encompass a wide spectrum of possibilities to challenge and evaluate existing methods to estimate connectivity scores. This may represent a critical gap in connectivity-based drug repurposing research because reliable benchmarking data are essential for assessing and advancing in the development of new algorithms. The simulation tool is available as a R package (General Public License (GPL) license) at https://github.com/cgonzalez-gomez/cosimu.

Infants' understanding of the causal power of agents and tools.

Tools are objects that are manipulated by agents with the intention to cause an effect in the world. We show that the cognitive capacity to understand tools is present in young infants, even if these tools produce arbitrary, causally opaque effects. In experiments 1-2, we used pupillometry to show that 8-mo-old infants infer an invisible causal contact to account for the-otherwise unexplained-motion of a ball. In experiments 3, we probed 8-mo-old infants' account of a state change event (flickering of a cube) that lies outside of the explanatory power of intuitive physics. Infants repeatedly watched an intentional agent launch a ball behind an occluder. After a short delay, a cube, positioned at the other end of the occluder began flickering. Rare unoccluded events served to probe infants' representation of what happened behind the occluder. Infants exhibited larger pupil dilation, signaling more surprise, when the ball stopped before touching the cube, than when it contacted the cube, suggesting that infants inferred that the cause of the state change was contact between the ball and the cube. This effect was canceled in experiment 4, when an inanimate sphere replaced the intentional agent. Altogether, results suggest that, in the infants' eyes, a ball (an inanimate object) has the power to cause an arbitrary state change, but only if it inherits this power from an intentional agent. Eight-month-olds are thus capable of representing complex event structures, involving an intentional agent causing a change with a tool.

Genetic dissection of intercellular interactions in vivo by membrane-permeable protein.

Unraveling cell-cell interaction is fundamental to understanding many biological processes. To date, genetic tools for labeling neighboring cells in mammals are not available. Here, we developed a labeling strategy based on the Cre-induced intercellular labeling protein (CILP). Cre-expressing donor cells release a lipid-soluble and membrane-permeable fluorescent protein that is then taken up by recipient cells, enabling fluorescent labeling of neighboring cells. Using CILP, we specifically labeled endothelial cells surrounding a special population of hepatocytes in adult mice and revealed their distinct gene signatures. Our results highlight the potential of CILP as a platform to reveal cell-cell interactions and communications in vivo.

The ground offers acoustic efficiency gains for crickets and other calling animals.

Male crickets attract females by producing calls with their forewings. Louder calls travel further and are more effective at attracting mates. However, crickets are much smaller than the wavelength of their call, and this limits their power output. A small group called tree crickets make acoustic tools called baffles which reduce acoustic short-circuiting, a source of dipole inefficiency. Here, we ask why baffling is uncommon among crickets. We hypothesize that baffling may be rare because like other tools they offer insufficient advantage for most species. To test this, we modelled the calling efficiencies of crickets within the full space of possible natural wing sizes and call frequencies, in multiple acoustic environments. We then generated efficiency landscapes, within which we plotted 112 cricket species across 7 phylogenetic clades. We found that all sampled crickets, in all conditions, could gain efficiency from tool use. Surprisingly, we also found that calling from the ground significantly increased efficiency, with or without a baffle, by as much as an order of magnitude. We found that the ground provides some reduction of acoustic short-circuiting but also halves the air volume within which sound is radiated. It simultaneously reflects sound upwards, allowing recapture of a significant amount of acoustic energy through constructive interference. Thus, using the ground as a reflective baffle is an effective strategy for increasing calling efficiency. Indeed, theory suggests that this increase in efficiency is accessible not just to crickets but to all acoustically communicating animals whether they are dipole or monopole sound sources.

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes.

Although transcriptomics data is typically used to analyze mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g. healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, ie reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, vs. state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. DifferentialRegulation is distributed as a Bioconductor R package.

FungiExpresZ: an intuitive package for fungal gene expression data analysis, visualization and discovery.

Bioinformatics analysis and visualization of high-throughput gene expression data require extensive computer programming skills, posing a bottleneck for many wet-lab scientists. In this work, we present an intuitive user-friendly platform for gene expression data analysis and visualization called FungiExpresZ. FungiExpresZ aims to help wet-lab scientists with little to no knowledge of computer programming to become self-reliant in bioinformatics analysis and generating publication-ready figures. The platform contains many commonly used data analysis tools and an extensive collection of pre-processed public ribonucleic acid sequencing (RNA-seq) datasets of many fungal species, including important human, plant and insect pathogens. Users may analyse their data alone or in combination with public RNA-seq data for an integrated analysis. The FungiExpresZ platform helps wet-lab scientists to overcome their limitations in genomics data analysis and can be applied to analyse data of any organism. FungiExpresZ is available as an online web-based tool (https://cparsania.shinyapps.io/FungiExpresZ/) and an offline R-Shiny package (https://github.com/cparsania/FungiExpresZ).

FFMAVP: a new classifier based on feature fusion and multitask learning for identifying antiviral peptides and their subclasses.

Antiviral peptides (AVPs) are widely found in animals and plants, with high specificity and strong sensitivity to drug-resistant viruses. However, due to the great heterogeneity of different viruses, most of the AVPs have specific antiviral activities. Therefore, it is necessary to identify the specific activities of AVPs on virus types. Most existing studies only identify AVPs, with only a few studies identifying subclasses by training multiple binary classifiers. We develop a two-stage prediction tool named FFMAVP that can simultaneously predict AVPs and their subclasses. In the first stage, we identify whether a peptide is AVP or not. In the second stage, we predict the six virus families and eight species specifically targeted by AVPs based on two multiclass tasks. Specifically, the feature extraction module in the two-stage task of FFMAVP adopts the same neural network structure, in which one branch extracts features based on amino acid feature descriptors and the other branch extracts sequence features. Then, the two types of features are fused for the following task. Considering the correlation between the two tasks of the second stage, a multitask learning model is constructed to improve the effectiveness of the two multiclass tasks. In addition, to improve the effectiveness of the second stage, the network parameters trained through the first-stage data are used to initialize the network parameters in the second stage. As a demonstration, the cross-validation results, independent test results and visualization results show that FFMAVP achieves great advantages in both stages.

In vivo photocontrol of orexin receptors with a nanomolar light-regulated analogue of orexin-B.

Orexinergic neurons are critically involved in regulating arousal, wakefulness, and appetite. Their dysfunction has been associated with sleeping disorders, and non-peptide drugs are currently being developed to treat insomnia and narcolepsy. Yet, no light-regulated agents are available to reversibly control their activity. To meet this need, a photoswitchable peptide analogue of the endogenous neuroexcitatory peptide orexin-B was designed, synthesized, and tested in vitro and in vivo. This compound - photorexin - is the first photo-reversible ligand reported for orexin receptors. It allows dynamic control of activity in vitro (including almost the same efficacy as orexin-B, high nanomolar potency, and subtype selectivity to human OX2 receptors) and in vivo in zebrafish larvae by direct application in water. Photorexin induces dose- and light-dependent changes in locomotion and a reduction in the successive induction reflex that is associated with sleep behavior. Molecular dynamics calculations indicate that trans and cis photorexin adopt similar bent conformations and that the only discriminant between their structures and activities is the positioning of the N-terminus. This, in the case of the more active trans isomer, points towards the OX2 N-terminus and extra-cellular loop 2, a region of the receptor known to be involved in ligand binding and recognition consistent with a "message-address" system. Thus, our approach could be extended to several important families of endogenous peptides, such as endothelins, nociceptin, and dynorphins among others, that bind to their cognate receptors through a similar mechanism: a "message" domain involved in receptor activation and signal transduction, and an "address" sequence for receptor occupation and improved binding affinity.

Inhibition of RAS-driven signaling and tumorigenesis with a pan-RAS monobody targeting the Switch I/II pocket.

RAS mutants are major therapeutic targets in oncology with few efficacious direct inhibitors available. The identification of a shallow pocket near the Switch II region on RAS has led to the development of small-molecule drugs that target this site and inhibit KRAS(G12C) and KRAS(G12D). To discover other regions on RAS that may be targeted for inhibition, we have employed small synthetic binding proteins termed monobodies that have a strong propensity to bind to functional sites on a target protein. Here, we report a pan-RAS monobody, termed JAM20, that bound to all RAS isoforms with nanomolar affinity and demonstrated limited nucleotide-state specificity. Upon intracellular expression, JAM20 potently inhibited signaling mediated by all RAS isoforms and reduced oncogenic RAS-mediated tumorigenesis in vivo. NMR and mutation analysis determined that JAM20 bound to a pocket between Switch I and II, which is similarly targeted by low-affinity, small-molecule inhibitors, such as BI-2852, whose in vivo efficacy has not been demonstrated. Furthermore, JAM20 directly competed with both the RAF(RBD) and BI-2852. These results provide direct validation of targeting the Switch I/II pocket for inhibiting RAS-driven tumorigenesis. More generally, these results demonstrate the utility of tool biologics as probes for discovering and validating druggable sites on challenging targets.

Correlation between the binding affinity and the conformational entropy of nanobody SARS-CoV-2 spike protein complexes.

Camelid single-domain antibodies, also known as nanobodies, can be readily isolated from naïve libraries for specific targets but often bind too weakly to their targets to be immediately useful. Laboratory-based genetic engineering methods to enhance their affinity, termed maturation, can deliver useful reagents for different areas of biology and potentially medicine. Using the receptor binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and a naïve library, we generated closely related nanobodies with micromolar to nanomolar binding affinities. By analyzing the structure-activity relationship using X-ray crystallography, cryoelectron microscopy, and biophysical methods, we observed that higher conformational entropy losses in the formation of the spike protein-nanobody complex are associated with tighter binding. To investigate this, we generated structural ensembles of the different complexes from electron microscopy maps and correlated the conformational fluctuations with binding affinity. This insight guided the engineering of a nanobody with improved affinity for the spike protein.

Computational tools and algorithms for ion mobility spectrometry-mass spectrometry.

Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.