A map of the rubisco biochemical landscape.

Rubisco is the primary CO2 fixing enzyme of the biosphere yet has slow kinetics. The roles of evolution and chemical mechanism in constraining the sequence landscape of rubisco remain debated. In order to map sequence to function, we developed a massively parallel assay for rubisco using an engineered E. coli where enzyme function is coupled to growth. By assaying >99% of single amino acid mutants across CO2 concentrations, we inferred enzyme velocity and CO2 affinity for thousands of substitutions. We identified many highly conserved positions that tolerate mutation and rare mutations that improve CO2 affinity. These data suggest that non-trivial kinetic improvements are readily accessible and provide a comprehensive sequence-to-function mapping for enzyme engineering efforts.

A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testing.

Upper tract urothelial carcinoma (UTUC) is a rare and aggressive, yet understudied, urothelial carcinoma (UC). The more frequent UC of the bladder comprises several molecular subtypes, associated with different targeted therapies and overlapping with protein-based subtypes. However, if and how these findings extend to UTUC remains unclear. Artificial intelligence-based approaches could help elucidate UTUC's biology and extend access to targeted treatments to a wider patient audience. Here, UTUC protein-based subtypes were identified, and a deep-learning (DL) workflow was developed to predict them directly from routine histopathological H&E slides. Protein-based subtypes in a retrospective cohort of 163 invasive tumors were assigned by hierarchical clustering of the immunohistochemical expression of three luminal (FOXA1, GATA3, and CK20) and three basal (CD44, CK5, and CK14) markers. Cluster analysis identified distinctive luminal (N = 80) and basal (N = 42) subtypes. The luminal subtype mostly included pushing, papillary tumors, whereas the basal subtype diffusely infiltrating, non-papillary tumors. DL model building relied on a transfer-learning approach by fine-tuning a pre-trained ResNet50. Classification performance was measured via three-fold repeated cross-validation. A mean area under the receiver operating characteristic curve of 0.83 (95% CI: 0.67-0.99), 0.8 (95% CI: 0.62-0.99), and 0.81 (95% CI: 0.65-0.96) was reached in the three repetitions. High-confidence DL-based predicted subtypes showed significant associations (p < 0.001) with morphological features, i.e. tumor type, histological subtypes, and infiltration type. Furthermore, a significant association was found with programmed cell death ligand 1 (PD-L1) combined positive score (p < 0.001) and FGFR3 mutational status (p = 0.002), with high-confidence basal predictions containing a higher proportion of PD-L1 positive samples and high-confidence luminal predictions a higher proportion of FGFR3-mutated samples. Testing of the DL model on an independent cohort highlighted the importance to accommodate histological subtypes. Taken together, our DL workflow can predict protein-based UTUC subtypes, associated with the presence of targetable alterations, directly from H&E slides.

Exploring the Protein Sequence Space with Global Generative Models.

Recent advancements in specialized large-scale architectures for training images and language have profoundly impacted the field of computer vision and natural language processing (NLP). Language models, such as the recent ChatGPT and GPT-4, have demonstrated exceptional capabilities in processing, translating, and generating human language. These breakthroughs have also been reflected in protein research, leading to the rapid development of numerous new methods in a short time, with unprecedented performance. Several of these models have been developed with the goal of generating sequences in novel regions of the protein space. In this work, we provide an overview of the use of protein generative models, reviewing (1) language models for the design of novel artificial proteins, (2) works that use non-transformer architectures, and (3) applications in directed evolution approaches.

[Chest Tube in Thoracic Trauma - Recommendations of the Interdisciplinary Thoracic Trauma Task Group of the German Society for Thoracic Surgery (DGT) and the German Trauma Society (DGU)]. / Thoraxdrainage beim Thoraxtrauma ­ Empfehlungen der interdisziplinären Arbeitsgemeinschaft Thoraxtrauma der Deutschen Gesellschaft für Thoraxchirurgie (DGT) und der Deutschen Gesellschaft für Unfallchirurgie (DGU).

For unstable patients with chest trauma, the chest tube is the method of choice for the treatment of a relevant pneumothorax or haemothorax. In the case of a tension pneumothorax, needle decompression with a cannula of at least 5 cm length should be performed, directly followed by the insertion of a chest tube. The evaluation of the patient should be performed primarily with a clinical examination, a chest X-ray and sonography, but the gold standard of diagnostic testing is computed tomography (CT).A small-bore chest tube (e.g. 14 French) should be used in stable patients, while unstable patients should receive a large-bore drain (24 French or larger). Insertion of chest drains has a high complication rate of between 5% and 25%, and incorrect positioning of the tube is the most common complication. However, incorrect positioning can usually only be reliably detected or ruled out with a CT scan, and chest X-rays proofed to be insufficient to answer this question. Therapy should be carried out with mild suction of approximately 20 cmH2O, and clamping the chest tube before removal showed no beneficial effect. The removal of drains can be safely performed, either at the end of inspiration or at the end of expiration. In order to reduce the high complication rate, in the future the focus should be more on the education and training of medical staff members.

[Occult and Retained Haemothorax - Recommendations of the Interdisciplinary Thoracic Trauma Task Group of the German Trauma Society (DGU - Section NIS) and the German Society for Thoracic Surgery (DGT)]. / Okkulter und residueller Hämatothorax nach Thoraxtrauma ­ Empfehlungen der interdisziplinären Arbeitsgruppe Thoraxtrauma der Sektion NIS der Deutschen Gesellschaft für Unfallchirurgie (DGU) und der Deutschen Gesellschaft für Thoraxchirurgie (DGT).

The management of occult and retained haemothorax is challenging for all involved in the care of polytrauma patients in terms of diagnosis and treatment. The focus of decision making is preventing sequelae such as pleural empyema and avoiding a trapped lung. An interdisciplinary task force of the German Society for Thoracic Surgery (DGT) and the German Trauma Society (DGU) on thoracic trauma offers recommendations for post-trauma care of patients with occult and/or retained haemothorax, as based on a comprehensive literature review.

Spaced words and kmacs: fast alignment-free sequence comparison based on inexact word matches.

In this article, we present a user-friendly web interface for two alignment-free sequence-comparison methods that we recently developed. Most alignment-free methods rely on exact word matches to estimate pairwise similarities or distances between the input sequences. By contrast, our new algorithms are based on inexact word matches. The first of these approaches uses the relative frequencies of so-called spaced words in the input sequences, i.e. words containing 'don't care' or 'wildcard' symbols at certain pre-defined positions. Various distance measures can then be defined on sequences based on their different spaced-word composition. Our second approach defines the distance between two sequences by estimating for each position in the first sequence the length of the longest substring at this position that also occurs in the second sequence with up to k mismatches. Both approaches take a set of deoxyribonucleic acid (DNA) or protein sequences as input and return a matrix of pairwise distance values that can be used as a starting point for clustering algorithms or distance-based phylogeny reconstruction. The two alignment-free programmes are accessible through a web interface at 'Göttingen Bioinformatics Compute Server (GOBICS)': http://spaced.gobics.de http://kmacs.gobics.de and the source codes can be downloaded.

Fast alignment-free sequence comparison using spaced-word frequencies.

MOTIVATION: Alignment-free methods for sequence comparison are increasingly used for genome analysis and phylogeny reconstruction; they circumvent various difficulties of traditional alignment-based approaches. In particular, alignment-free methods are much faster than pairwise or multiple alignments. They are, however, less accurate than methods based on sequence alignment. Most alignment-free approaches work by comparing the word composition of sequences. A well-known problem with these methods is that neighbouring word matches are far from independent. RESULTS: To reduce the statistical dependency between adjacent word matches, we propose to use 'spaced words', defined by patterns of 'match' and 'don't care' positions, for alignment-free sequence comparison. We describe a fast implementation of this approach using recursive hashing and bit operations, and we show that further improvements can be achieved by using multiple patterns instead of single patterns. To evaluate our approach, we use spaced-word frequencies as a basis for fast phylogeny reconstruction. Using real-world and simulated sequence data, we demonstrate that our multiple-pattern approach produces better phylogenies than approaches relying on contiguous words. AVAILABILITY AND IMPLEMENTATION: Our program is freely available at http://spaced.gobics.de/.