Ulla: a program for calculating environment-specific amino acid substitution tables

Summary: Amino acid residues are under various kinds of local environmental restraints, which influence substitution patterns. Ulla,1 a program for calculating environment-specific substitution tables, reads protein sequence alignments and local environment annotations. The program produces a substitution table for every possible combination of environment features. Sparse data is handled using an entropy-based smoothing procedure to estimate robust substitution probabilities

The dynamics of signal amplification by macromolecular assemblies for the control of chromosome segregation

The control of chromosome segregation relies on the spindle assembly checkpoint (SAC), a complex regulatory system that ensures the high fidelity of chromosome segregation in higher organisms by delaying the onset of anaphase until each chromosome is properly hi-oriented on the mitotic spindle. Central to this process is the establishment of multiple yet specific protein-protein interactions in a narrow time-space window. Here we discuss the highly dynamic nature of multi-protein complexes that control chromosome segregation in which an intricate network of weak but cooperative interactions modulate signal amplification to ensure a proper SAC response. We also discuss the current structural understanding of the communication between the SAC and the kinetochore; how transient interactions can regulate the assembly and disassembly of the SAC as well as the challenges and opportunities for the definition and the manipulation of the flow of information in SAC signalingopen

The dynamics of signal amplification by macromolecular assemblies for the control of chromosome segregation

The control of chromosome segregation relies on the spindle assembly checkpoint (SAC), a complex regulatory system that ensures the high fidelity of chromosome segregation in higher organisms by delaying the onset of anaphase until each chromosome is properly bi-oriented on the mitotic spindle. Central to this process is the establishment of multiple yet specific protein-protein interactions in a narrow time-space window. Here we discuss the highly dynamic nature of multi-protein complexes that control chromosome segregation in which an intricate network of weak but cooperative interactions modulate signal amplification to ensure a proper SAC response. We also discuss the current structural understanding of the communication between the SAC and the kinetochore; how transient interactions can regulate the assembly and disassembly of the SAC as well as the challenges and opportunities for the definition and the manipulation of the flow of information in SAC signaling

CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network

With recent advances in DNA sequencing technologies, fast acquisition of large-scale genomic data has become commonplace. For cancer studies, in particular, there is an increasing need for the classification of cancer type based on somatic alterations detected from sequencing analyses. However, the ever-increasing size and complexity of the data make the classification task extremely challenging. In this study, we evaluate the contributions of various input features, such as mutation profiles, mutation rates, mutation spectra and signatures, and somatic copy number alterations that can be derived from genomic data, and further utilize them for accurate cancer type classification. We introduce a novel ensemble of machine learning classifiers, called CPEM (Cancer Predictor using an Ensemble Model), which is tested on 7,002 samples representing over 31 different cancer types collected from The Cancer Genome Atlas (TCGA) database. We first systematically examined the impact of the input features. Features known to be associated with specific cancers had relatively high importance in our initial prediction model. We further investigated various machine learning classifiers and feature selection methods to derive the ensemble-based cancer type prediction model achieving up to 84% classification accuracy in the nested 10-fold cross-validation. Finally, we narrowed down the target cancers to the six most common types and achieved up to 94% accuracy

A lab-on-a-disc platform enables serial monitoring of individual CTCs associated with tumor progression during EGFR-targeted therapy for patients with NSCLC

Rationale: Unlike traditional biopsy, liquid biopsy, which is a largely non-invasive diagnostic and monitoring tool, can be performed more frequently to better track tumors and mutations over time and to validate the efficiency of a cancer treatment. Circulating tumor cells (CTCs) are considered promising liquid biopsy biomarkers; however, their use in clinical settings is limited by high costs and a low throughput of standard platforms for CTC enumeration and analysis. In this study, we used a label-free, high-throughput method for CTC isolation directly from whole blood of patients using a standalone, clinical setting-friendly platform. Methods: A CTC-based liquid biopsy approach was used to examine the efficacy of therapy and emergent drug resistance via longitudinal monitoring of CTC counts, DNA mutations, and single-cell-level gene expression in a prospective cohort of 40 patients with epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer. Results: The change ratio of the CTC counts was associated with tumor response, detected by CT scan, while the baseline CTC counts did not show association with progression-free survival or overall survival. We achieved a 100% concordance rate for the detection of EGFR mutation, including emergence of T790M, between tumor tissue and CTCs. More importantly, our data revealed the importance of the analysis of the epithelial/mesenchymal signature of individual pretreatment CTCs to predict drug responsiveness in patients. Conclusion: The fluid-assisted separation technology disc platform enables serial monitoring of CTC counts, DNA mutations, as well as unbiased molecular characterization of individual CTCs associated with tumor progression during targeted therapy

Towards End-to-End Generative Modeling of Long Videos with Memory-Efficient Bidirectional Transformers

Autoregressive transformers have shown remarkable success in video generation. However, the transformers are prohibited from directly learning the long-term dependency in videos due to the quadratic complexity of self-attention, and inherently suffering from slow inference time and error propagation due to the autoregressive process. In this paper, we propose Memory-efficient Bidirectional Transformer (MeBT) for end-to-end learning of long-term dependency in videos and fast inference. Based on recent advances in bidirectional transformers, our method learns to decode the entire spatio-temporal volume of a video in parallel from partially observed patches. The proposed transformer achieves a linear time complexity in both encoding and decoding, by projecting observable context tokens into a fixed number of latent tokens and conditioning them to decode the masked tokens through the cross-attention. Empowered by linear complexity and bidirectional modeling, our method demonstrates significant improvement over the autoregressive Transformers for generating moderately long videos in both quality and speed. Videos and code are available at https://sites.google.com/view/mebt-cvpr2023

Depression and suicide risk prediction models using blood-derived multi-omics data

More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R2 values of 0.961 and 0.943 for Hamilton Rating Scale for Depression???17 and Scale for Suicide Ideation, respectively. Multi-omics data were used to construct psychiatric status prediction models for improved mental health treatment

Electrically Conductive Polydopamine–Polypyrrole as High Performance Biomaterials for Cell Stimulation in Vitro and Electrical Signal Recording in Vivo

Conductive polymers (CPs) such as polypyrrole (PPY) are emerging biomaterials for use as scaffolds and bioelectrodes which interact with biological systems electrically. Still, more electrically conductive and biologically interactive CPs are required to develop high performance biomaterials and medical devices. In this study, in situ electrochemical copolymerization of polydopamine (PDA) and PPY were performed for electrode modification. Their material and biological properties were characterized using multiple techniques. The electrical properties of electrodes coated with PDA/PPY were superior to electrodes coated with PPY alone. The growth and differentiation of C2C12 myoblasts and PC12 neuronal cells on PDA/PPY was enhanced compared to PPY. Electrical stimulation of PC12 cells on PDA/PPY further promoted neuritogenesis. In vivo electromyography signal measurements demonstrated more sensitive signals from tibia muscles when using PDA/PPY-coated electrodes than bare or PPY-coated electrodes, revealing PDA/PPY to be a high-performance biomaterial with potential for various biomedical applications

Characterization of spindle checkpoint kinase Mps1 reveals domain with functional and structural similarities to tetratricopeptide repeat motifs of Bub1 and BubR1 checkpoint kinases.

Kinetochore targeting of the mitotic kinases Bub1, BubR1, and Mps1 has been implicated in efficient execution of their functions in the spindle checkpoint, the self-monitoring system of the eukaryotic cell cycle that ensures chromosome segregation occurs with high fidelity. In all three kinases, kinetochore docking is mediated by the N-terminal region of the protein. Deletions within this region result in checkpoint failure and chromosome segregation defects. Here, we use an interdisciplinary approach that includes biophysical, biochemical, cell biological, and bioinformatics methods to study the N-terminal region of human Mps1. We report the identification of a tandem repeat of the tetratricopeptide repeat (TPR) motif in the N-terminal kinetochore binding region of Mps1, with close homology to the tandem TPR motif of Bub1 and BubR1. Phylogenetic analysis indicates that TPR Mps1 was acquired after the split between deutorostomes and protostomes, as it is distinguishable in chordates and echinoderms. Overexpression of TPR Mps1 resulted in decreased efficiency of both chromosome alignment and mitotic arrest, likely through displacement of endogenous Mps1 from the kinetochore and decreased Mps1 catalytic activity. Taken together, our multidisciplinary strategy provides new insights into the evolution, structural organization, and function of Mps1 N-terminal region

Single-cell RNA sequencing reveals distinct cellular factors for response to immunotherapy targeting CD73 and PD-1 in colorectal cancer

Background Although cancer immunotherapy is one of the most effective advanced-stage cancer therapies, no clinically approved cancer immunotherapies currently exist for colorectal cancer (CRC). Recently, programmed cell death protein 1 (PD-1) blockade has exhibited clinical benefits according to ongoing clinical trials. However, ongoing clinical trials for cancer immunotherapies are focused on PD-1 signaling inhibitors such as pembrolizumab, nivolumab, and atezolizumab. In this study, we focused on revealing the distinct response mechanism for the potent CD73 ectoenzyme selective inhibitor AB680 as a promising drug candidate that functions by blocking tumorigenic ATP/adenosine signaling in comparison to current therapeutics that block PD-1 to assess the value of this drug as a novel immunotherapy for CRC. Methods To understand the distinct mechanism of AB680 in comparison to that of a neutralizing antibody against murine PD-1 used as a PD-1 blocker, we performed single-cell RNA sequencing of CD45(+) tumor-infiltrating lymphocytes from untreated controls (n=3) and from AB680-treated (n=3) and PD-1-blockade-treated murine CRC in vivo models. We also used flow cytometry, Azoxymethane (AOM)/Dextran Sulfate Sodium (DSS) models, and in vitro functional assays to validate our new findings. Results We initially observed that the expressions of Nt5e (a gene for CD73) and Entpd1 (a gene for CD39) affect T cell receptor (TCR) diversity and transcriptional profiles of T cells, thus suggesting their critical roles in T cell exhaustion within tumor. Importantly, PD-1 blockade significantly increased the TCR diversity of Entpd1-negative T cells and Pdcd1-positive T cells. Additionally, we determined that AB680 improved the anticancer functions of immunosuppressed cells such as Treg and exhausted T cells, while the PD-1 blocker quantitatively reduced Malat1(high) Treg and M2 macrophages. We also verified that PD-1 blockade induced Treg depletion in AOM/DSS CRC in vivo models, and we confirmed that AB680 treatment caused increased activation of CD8(+) T cells using an in vitro T cell assay. Conclusions The intratumoral immunomodulation of CD73 inhibition is distinct from PD-1 inhibition and exhibits potential as a novel anticancer immunotherapy for CRC, possibly through a synergistic effect when combined with PD-1 blocker treatments. This study may contribute to the ongoing development of anticancer immunotherapies targeting refractory CRC