BHLHE40 Regulates Myeloid Cell Polarization through IL-10-Dependent and -Independent Mechanisms.

Better understanding of the host responses to Mycobacterium tuberculosis infections is required to prevent tuberculosis and develop new therapeutic interventions. The host transcription factor BHLHE40 is essential for controlling M. tuberculosis infection, in part by repressing Il10 expression, where excess IL-10 contributes to the early susceptibility of Bhlhe40-/- mice to M. tuberculosis infection. Deletion of Bhlhe40 in lung macrophages and dendritic cells is sufficient to increase the susceptibility of mice to M. tuberculosis infection, but how BHLHE40 impacts macrophage and dendritic cell responses to M. tuberculosis is unknown. In this study, we report that BHLHE40 is required in myeloid cells exposed to GM-CSF, an abundant cytokine in the lung, to promote the expression of genes associated with a proinflammatory state and better control of M. tuberculosis infection. Loss of Bhlhe40 expression in murine bone marrow-derived myeloid cells cultured in the presence of GM-CSF results in lower levels of proinflammatory associated signaling molecules IL-1ß, IL-6, IL-12, TNF-α, inducible NO synthase, IL-2, KC, and RANTES, as well as higher levels of the anti-inflammatory-associated molecules MCP-1 and IL-10 following exposure to heat-killed M. tuberculosis. Deletion of Il10 in Bhlhe40-/- myeloid cells restored some, but not all, proinflammatory signals, demonstrating that BHLHE40 promotes proinflammatory responses via both IL-10-dependent and -independent mechanisms. In addition, we show that macrophages and neutrophils within the lungs of M. tuberculosis-infected Bhlhe40-/- mice exhibit defects in inducible NO synthase production compared with infected wild-type mice, supporting that BHLHE40 promotes proinflammatory responses in innate immune cells, which may contribute to the essential role for BHLHE40 during M. tuberculosis infection in vivo.

Large language models for science and medicine.

Large language models (LLMs) are a type of machine learning model that learn statistical patterns over text, such as predicting the next words in a sequence of text. Both general purpose and task-specific LLMs have demonstrated potential across diverse applications. Science and medicine have many data types that are highly suitable for LLMs, such as scientific texts (publications, patents and textbooks), electronic medical records, large databases of DNA and protein sequences and chemical compounds. Carefully validated systems that can understand and reason across all these modalities may maximize benefits. Despite the inevitable limitations and caveats of any new technology and some uncertainties specific to LLMs, LLMs have the potential to be transformative in science and medicine.

Generative artificial intelligence for de novo protein design.

Engineering new molecules with desirable functions and properties has the potential to extend our ability to engineer proteins beyond what nature has so far evolved. Advances in the so-called 'de novo' design problem have recently been brought forward by developments in artificial intelligence. Generative architectures, such as language models and diffusion processes, seem adept at generating novel, yet realistic proteins that display desirable properties and perform specified functions. State-of-the-art design protocols now achieve experimental success rates nearing 20%, thus widening the access to de novo designed proteins. Despite extensive progress, there are clear field-wide challenges, for example, in determining the best in silico metrics to prioritise designs for experimental testing, and in designing proteins that can undergo large conformational changes or be regulated by post-translational modifications. With an increase in the number of models being developed, this review provides a framework to understand how these tools fit into the overall process of de novo protein design. Throughout, we highlight the power of incorporating biochemical knowledge to improve performance and interpretability.

Scanorama: integrating large and diverse single-cell transcriptomic datasets.

Merging diverse single-cell RNA sequencing (scRNA-seq) data from numerous experiments, laboratories and technologies can uncover important biological insights. Nonetheless, integrating scRNA-seq data encounters special challenges when the datasets are composed of diverse cell type compositions. Scanorama offers a robust solution for improving the quality and interpretation of heterogeneous scRNA-seq data by effectively merging information from diverse sources. Scanorama is designed to address the technical variation introduced by differences in sample preparation, sequencing depth and experimental batches that can confound the analysis of multiple scRNA-seq datasets. Here we provide a detailed protocol for using Scanorama within a Scanpy-based single-cell analysis workflow coupled with Google Colaboratory, a cloud-based free Jupyter notebook environment service. The protocol involves Scanorama integration, a process that typically spans 0.5-3 h. Scanorama integration requires a basic understanding of cellular biology, transcriptomic technologies and bioinformatics. Our protocol and new Scanorama-Colaboratory resource should make scRNA-seq integration more widely accessible to researchers.

Unsupervised evolution of protein and antibody complexes with a structure-informed language model.

Large language models trained on sequence information alone can learn high-level principles of protein design. However, beyond sequence, the three-dimensional structures of proteins determine their specific function, activity, and evolvability. Here, we show that a general protein language model augmented with protein structure backbone coordinates can guide evolution for diverse proteins without the need to model individual functional tasks. We also demonstrate that ESM-IF1, which was only trained on single-chain structures, can be extended to engineer protein complexes. Using this approach, we screened about 30 variants of two therapeutic clinical antibodies used to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We achieved up to 25-fold improvement in neutralization and 37-fold improvement in affinity against antibody-escaped viral variants of concern BQ.1.1 and XBB.1.5, respectively. These findings highlight the advantage of integrating structural information to identify efficient protein evolution trajectories without requiring any task-specific training data.

Inverse folding of protein complexes with a structure-informed language model enables unsupervised antibody evolution.

Large language models trained on sequence information alone are capable of learning high level principles of protein design. However, beyond sequence, the three-dimensional structures of proteins determine their specific function, activity, and evolvability. Here we show that a general protein language model augmented with protein structure backbone coordinates and trained on the inverse folding problem can guide evolution for diverse proteins without needing to explicitly model individual functional tasks. We demonstrate inverse folding to be an effective unsupervised, structure-based sequence optimization strategy that also generalizes to multimeric complexes by implicitly learning features of binding and amino acid epistasis. Using this approach, we screened ~30 variants of two therapeutic clinical antibodies used to treat SARS-CoV-2 infection and achieved up to 26-fold improvement in neutralization and 37-fold improvement in affinity against antibody-escaped viral variants-of-concern BQ.1.1 and XBB.1.5, respectively. In addition to substantial overall improvements in protein function, we find inverse folding performs with leading experimental success rates among other reported machine learning-guided directed evolution methods, without requiring any task-specific training data.