A mouse DRG genetic toolkit reveals morphological and physiological diversity of somatosensory neuron subtypes.

Dorsal root ganglia (DRG) somatosensory neurons detect mechanical, thermal, and chemical stimuli acting on the body. Achieving a holistic view of how different DRG neuron subtypes relay neural signals from the periphery to the CNS has been challenging with existing tools. Here, we develop and curate a mouse genetic toolkit that allows for interrogating the properties and functions of distinct cutaneous targeting DRG neuron subtypes. These tools have enabled a broad morphological analysis, which revealed distinct cutaneous axon arborization areas and branching patterns of the transcriptionally distinct DRG neuron subtypes. Moreover, in vivo physiological analysis revealed that each subtype has a distinct threshold and range of responses to mechanical and/or thermal stimuli. These findings support a model in which morphologically and physiologically distinct cutaneous DRG sensory neuron subtypes tile mechanical and thermal stimulus space to collectively encode a wide range of natural stimuli.

Genetic manipulation of gut microbes enables single-gene interrogation in a complex microbiome.

Hundreds of microbiota genes are associated with host biology/disease. Unraveling the causal contribution of a microbiota gene to host biology remains difficult because many are encoded by nonmodel gut commensals and not genetically targetable. A general approach to identify their gene transfer methodology and build their gene manipulation tools would enable mechanistic dissections of their impact on host physiology. We developed a pipeline that identifies the gene transfer methods for multiple nonmodel microbes spanning five phyla, and we demonstrated the utility of their genetic tools by modulating microbiome-derived short-chain fatty acids and bile acids in vitro and in the host. In a proof-of-principle study, by deleting a commensal gene for bile acid synthesis in a complex microbiome, we discovered an intriguing role of this gene in regulating colon inflammation. This technology will enable genetically engineering the nonmodel gut microbiome and facilitate mechanistic dissection of microbiota-host interactions.

Molecular Epigenetics: Chemical Biology Tools Come of Age.

The field of epigenetics has exploded over the last two decades, revealing an astonishing level of complexity in the way genetic information is stored and accessed in eukaryotes. This expansion of knowledge, which is very much ongoing, has been made possible by the availability of evermore sensitive and precise molecular tools. This review focuses on the increasingly important role that chemistry plays in this burgeoning field. In an effort to make these contributions more accessible to the nonspecialist, we group available chemical approaches into those that allow the covalent structure of the protein and DNA components of chromatin to be manipulated, those that allow the activity of myriad factors that act on chromatin to be controlled, and those that allow the covalent structure and folding of chromatin to be characterized. The application of these tools is illustrated through a series of case studies that highlight how the molecular precision afforded by chemistry is being used to establish causal biochemical relationships at the heart of epigenetic regulation.

A cell-free transcription-translation pipeline for recreating methylation patterns boosts DNA transformation in bacteria.

The bacterial world offers diverse strains for understanding medical and environmental processes and for engineering synthetic biological chassis. However, genetically manipulating these strains has faced a long-standing bottleneck: how to efficiently transform DNA. Here, we report imitating methylation patterns rapidly in TXTL (IMPRINT), a generalized, rapid, and scalable approach based on cell-free transcription-translation (TXTL) to overcome DNA restriction, a prominent barrier to transformation. IMPRINT utilizes TXTL to express DNA methyltransferases from a bacterium's restriction-modification systems. The expressed methyltransferases then methylate DNA in vitro to match the bacterium's DNA methylation pattern, circumventing restriction and enhancing transformation. With IMPRINT, we efficiently multiplex methylation by diverse DNA methyltransferases and enhance plasmid transformation in gram-negative and gram-positive bacteria. We also develop a high-throughput pipeline that identifies the most consequential methyltransferases, and we apply IMPRINT to screen a ribosome-binding site library in a hard-to-transform Bifidobacterium. Overall, IMPRINT can enhance DNA transformation, enabling the use of sophisticated genetic manipulation tools across the bacterial world.

Single domain antibody: Development and application in biotechnology and biopharma.

Heavy-chain antibodies (HCAbs) are a unique type of antibodies devoid of light chains, and comprised of two heavy chains-only that recognize their cognate antigen by virtue of a single variable domain also referred to as VHH, single domain antibody (sdAb), or nanobody (Nb). These functional HCAbs, serendipitous discovered about three decades ago, are exclusively found in camelids, comprising dromedaries, camels, llamas, and vicugnas. Nanobodies have become an essential tool in biomedical research and medicine, both in diagnostics and therapeutics due to their beneficial properties: small size, high stability, strong antigen-binding affinity, low immunogenicity, low production cost, and straightforward engineering into more potent affinity reagents. The occurrence of HCAbs in camelids remains intriguing. It is believed to be an evolutionary adaptation, equipping camelids with a robust adaptive immune defense suitable to respond to the pressure from a pathogenic invasion necessitating a more profound antigen recognition and neutralization. This evolutionary innovation led to a simplified HCAb structure, possibly supported by genetic mutations and drift, allowing adaptive mutation and diversification in the heavy chain variable gene and constant gene regions. Beyond understanding their origins, the application of nanobodies has significantly advanced over the past 30 years. Alongside expanding laboratory research, there has been a rapid increase in patent application for nanobodies. The introduction of commercial nanobody drugs such as Cablivi, Nanozora, Envafolimab, and Carvykti has boosted confidence among in their potential. This review explores the evolutionary history of HCAbs, their ontogeny, and applications in biotechnology and pharmaceuticals, focusing on approved and ongoing medical research pipelines.

3.3 million years of stone tool complexity suggests that cumulative culture began during the Middle Pleistocene.

Cumulative culture, the accumulation of modifications, innovations, and improvements over generations through social learning, is a key determinant of the behavioral diversity across Homo sapiens populations and their ability to adapt to varied ecological habitats. Generations of improvements, modifications, and lucky errors allow humans to use technologies and know-how well beyond what a single naive individual could invent independently within their lifetime. The human dependence on cumulative culture may have shaped the evolution of biological and behavioral traits in the hominin lineage, including brain size, body size, life history, sociality, subsistence, and ecological niche expansion. Yet, we do not know when, in the human career, our ancestors began to depend on cumulative culture. Here, we show that hominins likely relied on a derived form of cumulative culture by at least ~600 kya, a result in line with a growing body of existing evidence. We analyzed the complexity of stone tool manufacturing sequences over the last 3.3 My of the archaeological record. We then compare these to the achievable complexity without cumulative culture, which we estimate using nonhuman primate technologies and stone tool manufacturing experiments. We find that archaeological technologies become significantly more complex than expected in the absence of cumulative culture only after ~600 kya.

The NYCKidSeq randomized controlled trial: Impact of GUÍA digitally enhanced genetic results disclosure in diverse families.

Digital solutions are needed to support rapid increases in the application of genetic/genomic tests (GTs) in diverse clinical settings and patient populations. We developed GUÍA, a bilingual digital application that facilitates disclosure of GT results. The NYCKidSeq randomized controlled trial enrolled diverse children with neurologic, cardiac, and immunologic conditions who underwent GTs. The trial evaluated GUÍA's impact on understanding the GT results by randomizing families to results disclosure genetic counseling with GUÍA (intervention) or standard of care (SOC). Parents/legal guardians (participants) completed surveys at baseline, post-results disclosure, and 6 months later. Survey measures assessed the primary study outcomes of participants' perceived understanding of and confidence in explaining their child's GT results and the secondary outcome of objective understanding. The analysis included 551 diverse participants, 270 in the GUÍA arm and 281 in SOC. Participants in the GUÍA arm had significantly higher perceived understanding post-results (OR = 2.8, CI[1.004, 7.617], p = 0.049) and maintained higher objective understanding over time (OR = 1.1, CI[1.004, 1.127], p = 0.038) compared to SOC. There was no impact on perceived confidence. Hispanic/Latino(a) individuals in the GUÍA arm maintained higher perceived understanding (OR = 3.9, CI[1.603, 9.254], p = 0.003), confidence (OR = 2.7, CI[1.021, 7.277], p = 0.046), and objective understanding (OR = 1.1, CI[1.009, 1.212], p = 0.032) compared to SOC. This trial demonstrates that GUÍA positively impacts understanding of GT results in diverse parents of children with suspected genetic conditions and builds a case for utilizing GUÍA to deliver complex results. Continued development and evaluation of digital applications in diverse populations are critical for equitably scaling GT offerings in specialty clinics.

Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits.

Integrative genetic association methods have shown great promise in post-GWAS (genome-wide association study) analyses, in which one of the most challenging tasks is identifying putative causal genes and uncovering molecular mechanisms of complex traits. Recent studies suggest that prevailing computational approaches, including transcriptome-wide association studies (TWASs) and colocalization analysis, are individually imperfect, but their joint usage can yield robust and powerful inference results. This paper presents INTACT, a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate putative causal genes. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly desirable feature, we further propose an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated probabilistic evidence. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. We apply the proposed methods to analyze the multi-tissue eQTL data from the GTEx project and eight large-scale complex- and molecular-trait GWAS datasets from multiple consortia and the UK Biobank. Overall, we find that the proposed methods markedly improve the existing putative gene implication methods and are particularly advantageous in evaluating and identifying key gene sets and biological pathways underlying complex traits.

A survey of experimental and computational identification of small proteins.

Small proteins (SPs) are typically characterized as eukaryotic proteins shorter than 100 amino acids and prokaryotic proteins shorter than 50 amino acids. Historically, they were disregarded because of the arbitrary size thresholds to define proteins. However, recent research has revealed the existence of many SPs and their crucial roles. Despite this, the identification of SPs and the elucidation of their functions are still in their infancy. To pave the way for future SP studies, we briefly introduce the limitations and advancements in experimental techniques for SP identification. We then provide an overview of available computational tools for SP identification, their constraints, and their evaluation. Additionally, we highlight existing resources for SP research. This survey aims to initiate further exploration into SPs and encourage the development of more sophisticated computational tools for SP identification in prokaryotes and microbiomes.

CPADS: a web tool for comprehensive pancancer analysis of drug sensitivity.

Drug therapy is vital in cancer treatment. Accurate analysis of drug sensitivity for specific cancers can guide healthcare professionals in prescribing drugs, leading to improved patient survival and quality of life. However, there is a lack of web-based tools that offer comprehensive visualization and analysis of pancancer drug sensitivity. We gathered cancer drug sensitivity data from publicly available databases (GEO, TCGA and GDSC) and developed a web tool called Comprehensive Pancancer Analysis of Drug Sensitivity (CPADS) using Shiny. CPADS currently includes transcriptomic data from over 29 000 samples, encompassing 44 types of cancer, 288 drugs and more than 9000 gene perturbations. It allows easy execution of various analyses related to cancer drug sensitivity. With its large sample size and diverse drug range, CPADS offers a range of analysis methods, such as differential gene expression, gene correlation, pathway analysis, drug analysis and gene perturbation analysis. Additionally, it provides several visualization approaches. CPADS significantly aids physicians and researchers in exploring primary and secondary drug resistance at both gene and pathway levels. The integration of drug resistance and gene perturbation data also presents novel perspectives for identifying pivotal genes influencing drug resistance. Access CPADS at https://smuonco.shinyapps.io/CPADS/ or https://robinl-lab.com/CPADS.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods.

Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

Clinical bioinformatics desiderata for molecular tumor boards.

Clinical Bioinformatics is a knowledge framework required to interpret data of medical interest via computational methods. This area became of dramatic importance in precision oncology, fueled by cancer genomic profiling: most definitions of Molecular Tumor Boards require the presence of bioinformaticians. However, all available literature remained rather vague on what are the specific needs in terms of digital tools and expertise to tackle and interpret genomics data to assign novel targeted or biomarker-driven targeted therapies to cancer patients. To fill this gap, in this article, we present a catalog of software families and human skills required for the tumor board bioinformatician, with specific examples of real-world applications associated with each element presented.

From pixels to insights: Machine learning and deep learning for bioimage analysis.

Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, enabling the automated, reproducible, and accurate analysis of biological images. Here, we provide an overview of the history and principles of machine learning and deep learning in the context of bioimage analysis. We discuss the essential steps of the bioimage analysis workflow, emphasizing how machine learning and deep learning have improved preprocessing, segmentation, feature extraction, object tracking, and classification. We provide examples that showcase the application of machine learning and deep learning in bioimage analysis. We examine user-friendly software and tools that enable biologists to leverage these techniques without extensive computational expertise. This review is a resource for researchers seeking to incorporate machine learning and deep learning in their bioimage analysis workflows and enhance their research in this rapidly evolving field.

Tools for investigating O-GlcNAc in signaling and other fundamental biological pathways.

Cells continuously fine-tune signaling pathway proteins to match nutrient and stress levels in their local environment by modifying intracellular proteins with O-linked N-acetylglucosamine (O-GlcNAc) sugars, an essential process for cell survival and growth. The small size of these monosaccharide modifications poses a challenge for functional determination, but the chemistry and biology communities have together created a collection of precision tools to study these dynamic sugars. This review presents the major themes by which O-GlcNAc influences signaling pathway proteins, including G-protein coupled receptors, growth factor signaling, mitogen-activated protein kinase (MAPK) pathways, lipid sensing, and cytokine signaling pathways. Along the way, we describe in detail key chemical biology tools that have been developed and applied to determine specific O-GlcNAc roles in these pathways. These tools include metabolic labeling, O-GlcNAc-enhancing RNA aptamers, fluorescent biosensors, proximity labeling tools, nanobody targeting tools, O-GlcNAc cycling inhibitors, light-activated systems, chemoenzymatic labeling, and nutrient reporter assays. An emergent feature of this signaling pathway meta-analysis is the intricate interplay between O-GlcNAc modifications across different signaling systems, underscoring the importance of O-GlcNAc in regulating cellular processes. We highlight the significance of O-GlcNAc in signaling and the role of chemical and biochemical tools in unraveling distinct glycobiological regulatory mechanisms. Collectively, our field has determined effective strategies to probe O-GlcNAc roles in biology. At the same time, this survey of what we do not yet know presents a clear roadmap for the field to use these powerful chemical tools to explore cross-pathway O-GlcNAc interactions in signaling and other major biological pathways.

Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria.

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as "supporting" level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (supporting, moderate, strong, very strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tool's scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying moderate and several reached strong evidence levels. One tool reached very strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.

A comprehensive assessment and comparison of tools for HLA class I peptide-binding prediction.

Human leukocyte antigen class I (HLA-I) molecules bind intracellular peptides produced by protein hydrolysis and present them to the T cells for immune recognition and response. Prediction of peptides that bind HLA-I molecules is very important in immunotherapy. A growing number of computational predictors have been developed in recent years. We survey a comprehensive collection of 27 tools focusing on their input and output data characteristics, key aspects of the underlying predictive models and their availability. Moreover, we evaluate predictive performance for eight representative predictors. We consider a wide spectrum of relevant aspects including allele-specific analysis, influence of negative to positive data ratios and runtime. We also curate high-quality benchmark datasets based on analysis of the consistency of the data labels. Results reveal that each considered method provides accurate results, which can be explained by our analysis that finds that their predictive models capture meaningful binding motifs. Although some methods are overall more accurate than others, we find that none of them is universally superior. We provide a comprehensive comparison of the convenience as well as the accuracy of the methods under specific prediction scenarios, such as for specific alleles, metrics of predictive performance and constraints on runtime. Our systematic and broad analysis provides informative clues to the users to identify the most suitable tools for a given prediction scenario and for the developers to design future methods.

A comprehensive review of bioinformatics tools for chromatin loop calling.

Precisely calling chromatin loops has profound implications for further analysis of gene regulation and disease mechanisms. Technological advances in chromatin conformation capture (3C) assays make it possible to identify chromatin loops in the genome. However, a variety of experimental protocols have resulted in different levels of biases, which require distinct methods to call true loops from the background. Although many bioinformatics tools have been developed to address this problem, there is still a lack of special introduction to loop-calling algorithms. This review provides an overview of the loop-calling tools for various 3C-based techniques. We first discuss the background biases produced by different experimental techniques and the denoising algorithms. Then, the completeness and priority of each tool are categorized and summarized according to the data source of application. The summary of these works can help researchers select the most appropriate method to call loops and further perform downstream analysis. In addition, this survey is also useful for bioinformatics scientists aiming to develop new loop-calling algorithms.

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools.

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations.

Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.