Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study.

The prognosis for gallbladder adenocarcinoma (GBAC), a highly malignant cancer, is not good. In order to facilitate individualized risk stratification and improve clinical decision-making, this study set out to create and validate a machine learning model that could accurately predict early survival outcomes in GBAC patients. Five models-RSF, Cox regression, GBM, XGBoost, and Deepsurv-were compared using data from the SEER database (2010-2020).

A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations.

To assist the translation of genetic findings to disease pathobiology and therapeutics discovery, we present an ensemble deep learning framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that predicts protein-binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms to generate comprehensive structurally informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods and experimentally validate its predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces and explore their impact on disease prognosis and drug responses.

Phosphoribosyl modification of poly-ubiquitin chains at the Legionella-containing vacuole prohibiting autophagy adaptor recognition.

Ubiquitination is a posttranslational modification in eukaryotes that plays a significant role in the infection of intracellular microbial pathogens, such as Legionella pneumophila. While the Legionella-containing vacuole (LCV) is coated with ubiquitin (Ub), it avoids recognition by autophagy adaptors. Here, we report that the Sdc and Sde families of effectors work together to build ubiquitinated species around the LCV.

Dissecting the -regulatory syntax of transcription initiation with deep learning.

Despite extensive characterization of mammalian Pol II transcription, the DNA sequence determinants of transcription initiation at a third of human promoters and most enhancers remain poorly understood. We trained and interpreted a neural network called ProCapNet that accurately models base-resolution initiation profiles from PRO-cap experiments using local DNA sequence. ProCapNet learns sequence motifs with distinct effects on initiation rates and TSS positioning and uncovers context-specific cryptic initiator elements intertwined within other TF motifs.

Preoperative Prediction of Metastatic Lymph Nodes Posterior to the Right Recurrent Laryngeal Nerve in cN0 Papillary Thyroid Carcinoma.

Background: The advantages of the dissecting the metastatic lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLN) remain a great deal of controversies in papillary thyroid carcinoma (PTC) patients without clinical evidence. The purpose of our retrospective research was to investigate the predictive factors of the LN-prRLN in cN0 PTC patients.

Methods And Materials: Altogether 251 consecutive cN0 PTC participants accepted unilateral or bilateral thyroidectomy accompanied with LN-prRLN dissection between June 2020 and May 2023 were included in the research.

Phosphoribosyl modification of poly-ubiquitin chains at the -containing vacuole prohibiting autophagy adaptor recognition.

Ubiquitination is a crucial posttranslational modification in eukaryotes that plays a significant role in the infection of intracellular microbial pathogens, such as the bacterium responsible for Legionnaires' disease. While the -containing vacuole (LCV) is coated with ubiquitin (Ub), it avoids recognition by autophagy adaptors. In this study, we report that the Sdc and Sde families of effectors work together to build ubiquitinated species around the LCV.

Modulating the organic photovoltaic properties of non-fullerene acceptors by molecular modification based on Y6: a theoretical study.

Developing non-fullerene acceptors (NFAs) by modifying the backbone, side chains and end groups is the most important strategy to improve the power conversion efficiency of organic solar cells (OSCs). Among numerous developed NFAs, Y6 and its derivatives are famous NFAs in the OSC field due to their good performance. Herein, in order to understand the mechanism of tuning the photovoltaic performance by modifying the Y6's center backbone, π-spacer and side-chains, we selected the PM6:Y6 OSC as a reference and systematically studied PM6:AQx-2, PM6:Y6-T, PM6:Y6-2T, PM6:Y6-O, PM6:Y6-1O and PM6:Y6-2O OSC systems based on extensive quantum chemistry calculations.

Finding Needles in the Haystack: Strategies for Uncovering Noncoding Regulatory Variants.

Despite accumulating evidence implicating noncoding variants in human diseases, unraveling their functionality remains a significant challenge. Systematic annotations of the regulatory landscape and the growth of sequence variant data sets have fueled the development of tools and methods to identify causal noncoding variants and evaluate their regulatory effects. Here, we review the latest advances in the field and discuss potential future research avenues to gain a more in-depth understanding of noncoding regulatory variants.

In vivo versus in silico assessment of potentially pathogenic missense variants in human reproductive genes.

Infertility is a heterogeneous condition, with genetic causes thought to underlie a substantial fraction of cases. Genome sequencing is becoming increasingly important for genetic diagnosis of diseases including idiopathic infertility; however, most rare or minor alleles identified in patients are variants of uncertain significance (VUS). Interpreting the functional impacts of VUS is challenging but profoundly important for clinical management and genetic counseling.

Submitochondrial Protein Translocation Upon Stress Inhibits Thermogenic Energy Expenditure.

Unlabelled: Mitochondria-rich brown adipocytes dissipate cellular fuel as heat by thermogenic energy expenditure (TEE). Prolonged nutrient excess or cold exposure impair TEE and contribute to the pathogenesis of obesity, but the mechanisms remain incompletely understood. Here we report that stress-induced proton leak into the matrix interface of mitochondrial innermembrane (IM) mobilizes a group of proteins from IM into matrix, which in turn alter mitochondrial bioenergetics.

Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations.

Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (rotein-protein nteractin itrfac pediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes.

A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology.

A major goal of cancer biology is to understand the mechanisms underlying tumorigenesis driven by somatically acquired mutations. Two distinct types of computational methodologies have emerged: one focuses on analyzing clustering of mutations within protein sequences and 3D structures, while the other characterizes mutations by leveraging the topology of protein-protein interaction network. Their insights are largely non-overlapping, offering complementary strengths.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets.

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients.

Functional genomic assays to annotate enhancer-promoter interactions genome wide.

Enhancers are pivotal for regulating gene transcription that occurs at promoters. Identification of the interacting enhancer-promoter pairs and understanding the mechanisms behind how they interact and how enhancers modulate transcription can provide fundamental insight into gene regulatory networks. Recently, advances in high-throughput methods in three major areas-chromosome conformation capture assay, such as Hi-C to study basic chromatin architecture, ectopic reporter experiments such as self-transcribing active regulatory region sequencing (STARR-seq) to quantify promoter and enhancer activity, and endogenous perturbations such as clustered regularly interspaced short palindromic repeat interference (CRISPRi) to identify enhancer-promoter compatibility-have further our knowledge about transcription.

Progranulin-derived granulin E and lysosome membrane protein CD68 interact to reciprocally regulate their protein homeostasis.

Progranulin (PGRN) is a glycoprotein implicated in several neurodegenerative diseases. It is highly expressed in microglia and macrophages and can be secreted or delivered to the lysosome compartment. PGRN comprises 7.

DNMT1-mediated epigenetic silencing of TRAF6 promotes prostate cancer tumorigenesis and metastasis by enhancing EZH2 stability.

A plethora of studies have shown that both DNMT1 and EZH2 have great effects on the progression of a variety of cancers. However, it remains unclear whether the expression profiles of these two epigenetic enzymes are molecularly intertwined in prostate cancer (PC), especially in castration-resistant prostate cancer (CRPC). Here, we found that DNMT1 is highly expressed and facilitates PC cell proliferation and migration.

A comprehensive SARS-CoV-2-human protein-protein interactome network identifies pathobiology and host-targeting therapies for COVID-19.

Physical interactions between viral and host proteins are responsible for almost all aspects of the viral life cycle and the host's immune response. Studying viral-host protein-protein interactions is thus crucial for identifying strategies for treatment and prevention of viral infection. Here, we use high-throughput yeast two-hybrid and affinity purification followed by mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of both binary and co-complex interactions.

Advancing discovery of risk-altering variants for complex diseases by functionally informed fine-mapping.

Pinpointing causal variants at risk loci identified by genome-wide association studies (GWAS) has been a great challenge. In this issue of Neuron, Zhang et al. present a fine-mapping approach, RefMap, integrating functional genomics with GWAS summary statistics to prioritize causal variants for amyotrophic lateral sclerosis.

A comparison of experimental assays and analytical methods for genome-wide identification of active enhancers.

Mounting evidence supports the idea that transcriptional patterns serve as more specific identifiers of active enhancers than histone marks; however, the optimal strategy to identify active enhancers both experimentally and computationally has not been determined. Here, we compared 13 genome-wide RNA sequencing (RNA-seq) assays in K562 cells and show that nuclear run-on followed by cap-selection assay (GRO/PRO-cap) has advantages in enhancer RNA detection and active enhancer identification. We also introduce a tool, peak identifier for nascent transcript starts (PINTS), to identify active promoters and enhancers genome wide and pinpoint the precise location of 5' transcription start sites.

Deep learning methods for 3D structural proteome and interactome modeling.

Bolstered by recent methodological and hardware advances, deep learning has increasingly been applied to biological problems and structural proteomics. Such approaches have achieved remarkable improvements over traditional machine learning methods in tasks ranging from protein contact map prediction to protein folding, prediction of protein-protein interaction interfaces, and characterization of protein-drug binding pockets. In particular, emergence of ab initio protein structure prediction methods including AlphaFold2 has revolutionized protein structural modeling.

Aging-related cell type-specific pathophysiologic immune responses that exacerbate disease severity in aged COVID-19 patients.

Coronavirus disease 2019 (COVID-19) is especially severe in aged patients, defined as 65 years or older, for reasons that are currently unknown. To investigate the underlying basis for this vulnerability, we performed multimodal data analyses on immunity, inflammation, and COVID-19 incidence and severity as a function of age. Our analysis leveraged age-specific COVID-19 mortality and laboratory testing from a large COVID-19 registry, along with epidemiological data of ~3.

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers.

Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations.

Implications of disease-related mutations at protein-protein interfaces.

Protein-protein interfaces have been attracting great attention owing to their critical roles in protein-protein interactions and the fact that human disease-related mutations are generally enriched in them. Recently, substantial research progress has been made in this field, which has significantly promoted the understanding and treatment of various human diseases. For example, many studies have discovered the properties of disease-related mutations.

A 3D structural SARS-CoV-2-human interactome to explore genetic and drug perturbations.

Emergence of new viral agents is driven by evolution of interactions between viral proteins and host targets. For instance, increased infectivity of SARS-CoV-2 compared to SARS-CoV-1 arose in part through rapid evolution along the interface between the spike protein and its human receptor ACE2, leading to increased binding affinity. To facilitate broader exploration of how pathogen-host interactions might impact transmission and virulence in the ongoing COVID-19 pandemic, we performed state-of-the-art interface prediction followed by molecular docking to construct a three-dimensional structural interactome between SARS-CoV-2 and human.