Ecological significance of Candidatus ARS69 and Gemmatimonadota in the Arctic glacier foreland ecosystems

The Gemmatimonadota phylum has been widely detected in diverse natural environments, yet their specific ecological roles in many habitats remain poorly investigated. Similarly, the Candidatus ARS69 phylum has been identified only in a few habitats, and literature on their metabolic functions is relatively scarce. In the present study, we investigated the ecological significance of phyla Ca. ARS69 and Gemmatimonadota in the Arctic glacier foreland (GF) ecosystems through genome-resolved metagenomics. We have reconstructed the first high-quality metagenome-assembled genome (MAG) belonging to Ca. ARS69 and 12 other MAGs belonging to phylum Gemmatimonadota from the three different Arctic GF samples. We further elucidated these two groups phylogenetic lineage and their metabolic function through phylogenomic and pangenomic analysis. The analysis showed that all the reconstructed MAGs potentially belonged to novel species. The MAGs belonged to Ca. ARS69 consist about 8296 gene clusters, of which only about 8% of single-copy core genes (n = 980) were shared among them. The study also revealed the potential ecological role of Ca. ARS69 is associated with carbon fixation, denitrification, sulfite oxidation, and reduction biochemical processes in the GF ecosystems. Similarly, the study demonstrates the widespread distribution of different classes of Gemmatimonadota across wide ranges of ecosystems and their metabolic functions, including in the polar region. KEY POINTS: • Glacier foreland ecosystems act as a natural laboratory to study microbial community structure. • We have reconstructed 13 metagenome-assembled genomes from the soil samples. • All the reconstructed MAGs belonged to novel species with different metabolic processes. • Ca. ARS69 and Gemmatimonadota MAGs were found to participate in carbon fixation and denitrification processes.

Metabolic engineering of Shewanella oneidensis to produce glutamate and itaconic acid

Shewanella oneidensis is a gram-negative bacterium known for its unique respiratory capabilities, which allow it to utilize a wide range of electron acceptors, including solid substrates such as electrodes. For a future combination of chemical production and electro-fermentation, the goal of this study was to expand its product spectrum. S. oneidensis was metabolically engineered to optimize its glutamate production and to enable production of itaconic acid. By deleting the glutamate importer gltS for a reduced glutamate uptake and pckA/ptA to redirect the carbon flux towards the TCA cycle, a ∆3 mutant was created. In combination with the plasmid pG2 carrying the glutamate dehydrogenase gdhA and a specific glutamate exporter NCgl1221 A111V, a 72-fold increase in glutamate concentration compared to the wild type was achieved. Along with overexpression of gdhA and NCgl1221 A111V, the deletion of gltS and pckA/ptA as well as the deletion of all three genes (∆3) was examined for their impact on growth and lactate consumption. This showed that the redirection of the carbon flux towards the TCA cycle is possible. Furthermore, we were able to produce itaconic acid for the first time with a S. oneidensis strain. A titer of 7 mM was achieved after 48 h. This suggests that genetic optimization with an expression vector carrying a cis-aconitate decarboxylase (cadA) and a aconitate hydratase (acnB) along with the proven redirection of the carbon flux to the TCA cycle enabled the production of itaconic acid, a valuable platform chemical used in the production of a variety of products. KEY POINTS: •Heterologous expression of gdhA and NCgl1221_A111V leads to higher glutamate production. •Deletion of ackA/pta redirects carbon flux towards TCA cycle. •Heterologous expression of cadA and acnB enables itaconic acid production.

Response of particle-attached and free-living bacterial communities to Microcystis blooms

The massive proliferation of Microcystis threatens freshwater ecosystems and degrades water quality globally. Understanding the mechanisms that contribute to Microcystis growth is crucial for managing Microcystis blooms. The lifestyles of bacteria can be classified generally into two groups: particle-attached (PA; > 3 µm) and free-living (FL; 0.2-3.0 µm). However, little is known about the response of PA and FL bacteria to Microcystis blooms. Using 16S rRNA gene high-throughput sequencing, we investigated the stability, assembly process, and co-occurrence patterns of PA and FL bacterial communities during distinct bloom stages. PA bacteria were phylogenetically different from their FL counterparts. Microcystis blooms substantially influenced bacterial communities. The time decay relationship model revealed that Microcystis blooms might increase the stability of both PA and FL bacterial communities. A contrasting community assembly mechanism was observed between the PA and FL bacterial communities. Throughout Microcystis blooms, homogeneous selection was the major assembly process that impacted the PA bacterial community, whereas drift explained much of the turnover of the FL bacterial community. Both PA and FL bacterial communities could be separated into modules related to different phases of Microcystis blooms. Microcystis blooms altered the assembly process of PA and FL bacterial communities. PA bacterial community appeared to be more responsive to Microcystis blooms than FL bacteria. Decomposition of Microcystis blooms may enhance cooperation among bacteria. Our findings highlight the importance of studying bacterial lifestyles to understand their functions in regulating Microcystis blooms. KEY POINTS: • Microcystis blooms alter the assembly process of PA and FL bacterial communities • Microcystis blooms increase the stability of both PA and FL bacterial communities • PA bacteria seem to be more responsive to Microcystis blooms than FL bacteria.

iSanXoT: A standalone application for the integrative analysis of mass spectrometry-based quantitative proteomics data

Many bioinformatics tools are available for the quantitative analysis of proteomics experiments. Most of these tools use a dedicated statistical model to derive absolute quantitative protein values from mass spectrometry (MS) data. Here, we present iSanXoT, a standalone application that processes relative abundances between MS signals and then integrates them sequentially to upper levels using the previously published Generic Integration Algorithm (GIA). iSanXoT offers unique capabilities that complement conventional quantitative software applications, including statistical weighting and independent modeling of error distributions in each integration, aggregation of technical or biological replicates, quantification of posttranslational modifications, and analysis of coordinated protein behavior. iSanXoT is a standalone, user-friendly application that accepts output from popular proteomics pipelines and enables unrestricted creation of quantification workflows and fully customizable reports that can be reused across projects or shared among users. Numerous publications attest the successful application of diverse integrative workflows constructed using the GIA for the analysis of high-throughput quantitative proteomics experiments. iSanXoT has been tested with the main operating systems. Download links for the corresponding distributions are available at https://github.com/CNIC-Proteomics/iSanXoT/releases.

Large intestinal nutritional and physicochemical parameters from different dog sizes reshape canine microbiota structure and functions in vitro

Different dog sizes are associated with variations in large intestinal physiology including gut microbiota, which plays a key role in animal health. This study aims to evaluate, using the CANIM-ARCOL (Canine Mucosal Artificial Colon), the relative importance of gut microbes versus physicochemical and nutritional parameters of the canine colonic environment in shaping microbiota structure and functions. CANIM-ARCOL was set up to reproduce nutrient availability, bile acid profiles, colonic pH, and transit time from small, medium, or large dogs according to in vivo data, while bioreactors were all inoculated with a fecal sample collected from medium size dogs (n = 2). Applying different dog size parameters resulted in a positive association between size and gas or SCFA production, as well as distinct microbiota profiles as revealed by 16S Metabarcoding. Comparisons with in vivo data from canine stools and previous in vitro results obtained when CANIM-ARCOL was inoculated with fecal samples from three dog sizes revealed that environmental colonic parameters were sufficient to drive microbiota functions. However, size-related fecal microbes were necessary to accurately reproduce in vitro the colonic ecosystem of small, medium, and large dogs. For the first time, this study provides mechanistic insights on which parameters from colonic ecosystem mainly drive canine microbiota in relation to dog size. The CANIM-ARCOL can be used as a relevant in vitro platform to unravel interactions between food or pharma compounds and canine colonic microbiota, under different dog size conditions. The potential of the model will be extended soon to diseased situations (e.g. chronic enteropathies or obesity).

A multi-omic single-cell landscape of cellular diversification in the developing human cerebral cortex

The vast neuronal diversity in the human neocortex is vital for high-order brain functions, necessitating elucidation of the regulatory mechanisms underlying such unparalleled diversity. However, recent studies have yet to comprehensively reveal the diversity of neurons and the molecular logic of neocortical origin in humans at single-cell resolution through profiling transcriptomic or epigenomic landscapes, owing to the application of unimodal data alone to depict exceedingly heterogeneous populations of neurons. In this study, we generated a comprehensive compendium of the developing human neocortex by simultaneously profiling gene expression and open chromatin from the same cell. We computationally reconstructed the differentiation trajectories of excitatory projection neurons of cortical origin and inferred the regulatory logic governing lineage bifurcation decisions for neuronal diversification. We demonstrated that neuronal diversity arises from progenitor cell lineage specificity and postmitotic differentiation at distinct stages. Our data paves the way for understanding the primarily coordinated regulatory logic for neuronal diversification in the neocortex.

Benchmarking tools for transcription factor prioritization

Spatiotemporal regulation of gene expression is controlled by transcription factor (TF) binding to regulatory elements, resulting in a plethora of cell types and cell states from the same genetic information. Due to the importance of regulatory elements, various sequencing methods have been developed to localise them in genomes, for example using ChIP-seq profiling of the histone mark H3K27ac that marks active regulatory regions. Moreover, multiple tools have been developed to predict TF binding to these regulatory elements based on DNA sequence. As altered gene expression is a hallmark of disease phenotypes, identifying TFs driving such gene expression programs is critical for the identification of novel drug targets. In this study, we curated 84 chromatin profiling experiments (H3K27ac ChIP-seq) where TFs were perturbed through e.g., genetic knockout or overexpression. We ran nine published tools to prioritize TFs using these real-world datasets and evaluated the performance of the methods in identifying the perturbed TFs. This allowed the nomination of three frontrunner tools, namely RcisTarget, MEIRLOP and monaLisa. Our analyses revealed opportunities and commonalities of tools that will help to guide further improvements and developments in the field.

The complete catalog of antimicrobial resistance secondary active transporters in Clostridioides difficile: evolution and drug resistance perspective

Secondary active transporters shuttle substrates across eukaryotic and prokaryotic membranes, utilizing different electrochemical gradients. They are recognized as one of the antimicrobial efflux pumps among pathogens. While primary active transporters within the genome of C. difficile 630 have been completely cataloged, the systematical study of secondary active transporters remains incomplete. Here, we not only identify secondary active transporters but also disclose their evolution and role in drug resistance in C. difficile 630. Our analysis reveals that C. difficile 630 carries 147 secondary active transporters belonging to 27 (super)families. Notably, 50 (34%) of them potentially contribute to antimicrobial resistance (AMR). AMR-secondary active transporters are structurally classified into five (super)families: the p-aminobenzoyl-glutamate transporter (AbgT), drug/metabolite transporter (DMT) superfamily, major facilitator (MFS) superfamily, multidrug and toxic compound extrusion (MATE) family, and resistance-nodulation-division (RND) family. Surprisingly, complete RND genes found in C. difficile 630 are likely an evolutionary leftover from the common ancestor with the diderm. Through protein structure comparisons, we have potentially identified six novel AMR-secondary active transporters from DMT, MATE, and MFS (super)families. Pangenome analysis revealed that half of the AMR-secondary transporters are accessory genes, which indicates an important role in adaptive AMR function rather than innate physiological homeostasis. Gene expression profile firmly supports their ability to respond to a wide spectrum of antibiotics. Our findings highlight the evolution of AMR-secondary active transporters and their integral role in antibiotic responses. This marks AMR-secondary active transporters as interesting therapeutic targets to synergize with other antibiotic activity.

Downregulation of ABLIM3 confers to the metastasis of neuroblastoma via regulating the cell adhesion molecules pathway

Neuroblastoma (NB) is the most prevalent extracranial solid tumor in pediatric patients, and its treatment failure often associated with metastasis. In this study, LASSO, SVM-RFE, and random forest tree algorithms, was used to identify the pivotal gene involved in NB metastasis. NB cell lines (SK-N-AS and SK-N-BE2), in conjunction with NB tissue were used for further study. ABLIM3 was identified as the hub gene and can be an independent prognostic factor for patients with NB. The immunohistochemical analysis revealed that ABLIM3 is negatively correlated with the metastasis of NB. Patients with low expression of ABLIM3 had a poor prognosis. High ABLIM3 expression correlated with APC co-stimulation and Type1 IFN response, and TIDE analysis indicated that patients with low ABLIM3 expression exhibited enhanced responses to immunotherapy. Downregulation of ABLIM3 by shRNA transfection increased the migration and invasion ability of NB cells. Gene Set Enrichment Analysis (GSEA) revealed that genes associated with ABLIM3 were primarily enriched in the cell adhesion molecules (CAMs) pathway. RT-qPCR and western blot analyses demonstrated that downregulation of ABLIM3 led to decreased expression of ITGA3, ITGA8, and KRT19, the key components of CAMs. This study indicated that ABLIM3 can be an independent prognostic factor for NB patients, and CAMs may mediate the effect of ABLIM3 on the metastasis of NB, suggesting that ABLIM3 is a potential therapeutic target for NB metastasis, which provides a novel strategy for future research and treatment strategies for NB patients.

GeTeSEPdb: A comprehensive database and online tool for the identification and analysis of gene profiles with temporal-specific expression patterns

Gene expression is dynamic and varies at different stages of processes. The identification of gene profiles with temporal-specific expression patterns can provide valuable insights into ongoing biological processes, such as the cell cycle, cell development, circadian rhythms, or responses to external stimuli such as drug treatments or viral infections. However, currently, no database defines, identifies or archives gene profiles with temporal-specific expression patterns. Here, using a high-throughput regression analysis approach, eight linear and nonlinear parametric models were fitted to gene expression profiles from time-series experiments to identify eight types of gene profiles with temporal-specific expression patterns. We curated 2684 time-series transcriptome datasets and identified 2644,370 gene profiles exhibiting temporal-specific expression patterns. The results were stored in the database GeTeSEPdb (gene profiles with temporal-specific expression patterns database, http://www.inbirg.com/GeTeSEPdb/). Moreover, we implemented an online tool to identify gene profiles with temporal-specific expression patterns from user-submitted data. In summary, GeTeSEPdb is a comprehensive web service that can be used to identify and analyse gene profiles with temporal-specific expression patterns. This approach facilitates the exploration of transcriptional changes and temporal patterns of responses. We firmly believe that GeTeSEPdb will become a valuable resource for biologists and bioinformaticians.

Identification of MORN3 and LLGL2 as novel diagnostic biomarkers for latent tuberculosis infection using machine learning strategies and experimental verification

BACKGROUND

Current diagnostic methods cannot effectively distinguish between latent tuberculosis infection (LTBI) and active tuberculosis (ATB). This study aims to explore novel non-invasive diagnostic biomarkers for LTBI and to elucidate possible molecular mechanisms of LTBI pathogenesis.

METHODS

Three GEO datasets (GSE19439, GSE19444, and GSE62525) were utilized to analyze the differentially expressed genes (DEGs). Functional enrichment studies were then performed on these DEGs. To ascertain potential diagnostic biomarkers, we utilized two different machine learning techniques: LASSO and RF. ROC curves were constructed in both the training and validation datasets to assess the diagnostic efficacy. The expression of identified biomarkers was verified by RT-qPCR in our own Chinese cohort. Using CIBERSORT, we estimated the abundances of 22 immune cell types in LTBI group, and subsequently analyzed the relationship between biomarker expression and immune cell infiltration.

RESULTS

166 DEGs were identified between ATB and LTBI groups, which are primarily associated with immune responses, inflammatory signaling pathways, and infection factors. Following that, 22 candidate diagnostic biomarkers for LTBI were selected in the machine learning process. Three up-regulated genes, MORN3, LLGL2, and IFT140, whose expression levels were not previously reported in TB, were validated using the training and validation cohort datasets. In our own Chinese cohort, we also found that MORN3 and LLGL2 showed good diagnostic effect using RT-qPCR method. Finally, we revealed the specific infiltration features of immune cells in LTBI and observed a notable correlation between potential marker expression and immune cells.

CONCLUSIONS

MORN3 and LLGL2 emerged as candidate diagnostic biomarkers for LTBI, following the elucidation of the key immune cell types involved. Our findings will contribute to providing a potential target for early noninvasive diagnosis of LTBI patients.

Single-cell transcriptomic analysis uncovers heterogeneity in the labial gland microenvironment of primary Sjögren's syndrome

Objective

Primary Sjögren's syndrome (pSS) is a systemic autoimmune disorder with an unclear pathogenetic mechanism in the labial gland. This study aims to investigate the cellular and molecular mechanisms contributing to the development of this disease.

Methods

Single-cell RNA sequencing (scRNA-seq) was performed on 32,337 cells of labial glands from three pSS patients and three healthy individuals. We analyzed all cell subsets implicated in pSS pathogenesis.

Results

Our research revealed diminished differentiation among epithelial cells, concomitant with an enhancement of interferons (IFNs)-mediated signaling pathways. This indicates a cellular functional shift in reaction to inflammatory triggers. Moreover, we observed an augmentation in the population of myofibroblasts and endothelial cells, likely due to the intensified IFNs signaling, suggesting a possible reconfiguration of tissue structure and vascular networks in the impacted regions. Within the immune landscape, there was an apparent increase in immunosuppressive macrophages and dendritic cells (DCs), pointing to an adaptive immune mechanism aimed at modulating inflammation and averting excessive tissue harm. Elevated activation levels of CD4+T cells, along with a rise in regulatory T (Treg) cells, were noted, indicating a nuanced immune interplay designed to manage the inflammatory response. In the CD8+T cell subsests, we detected a notable increase in cells expressing granzyme K (GZMK), signaling an intensified cytotoxic activity. Additionally, the escalated presence of T cells with high levels of heat shock proteins (HSPs) suggests a cellular stress condition, possibly associated with persistent low-grade inflammation, mirroring the chronic aspect of the condition.

Conclusions

Our research identified distinct stromal and immune cell populations linked to pSS, revealing new potential targets for its management. The activation of myeloid, B, and T cells could contribute to pSS pathogenesis, providing important guidance for therapeutic approaches.

Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis

Background

Inflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis.

Methods

We retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm.

Findings

The model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively (P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line.

Conclusion

In this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melanoma.

Uncovering the potential of APOD as a biomarker in gastric cancer: A retrospective and multi-center study

Gastric cancer (GC) poses a significant health challenge worldwide, necessitating the identification of predictive biomarkers to improve prognosis. Dysregulated lipid metabolism is a well-recognized hallmark of tumorigenesis, prompting investigation into apolipoproteins (APOs). In this study, we focused on apolipoprotein D (APOD) following comprehensive analyses of APOs in pan-cancer. Utilizing data from the TCGA-STAD and GSE62254 cohorts, we elucidated associations between APOD expression and multiple facets of GC, including prognosis, tumor microenvironment (TME), cancer biomarkers, mutations, and immunotherapy response, and identified potential anti-GC drugs. Single-cell analyses and immunohistochemical staining confirmed APOD expression in fibroblasts within the GC microenvironment. Additionally, we independently validated the prognostic significance of APOD in the ZN-GC cohort. Our comprehensive analyses revealed that high APOD expression in GC patients was notably associated with unfavorable clinical outcomes, reduced microsatellite instability and tumor mutation burden, alterations in the TME, and diminished response to immunotherapy. These findings provide valuable insights into the potential prognostic and therapeutic implications of APOD in GC.

Smartphone-based machine learning model for real-time assessment of medical kidney biopsy

Background

Kidney biopsy is the gold-standard for diagnosing medical renal diseases, but the accuracy of the diagnosis greatly depends on the quality of the biopsy specimen, particularly the amount of renal cortex obtained. Inadequate biopsies, characterized by insufficient cortex or predominant medulla, can lead to inconclusive or incorrect diagnoses, and repeat biopsy. Unfortunately, there has been a concerning increase in the rate of inadequate kidney biopsies, and not all medical centers have access to trained professionals who can assess biopsy adequacy in real time. In response to this challenge, we aimed to develop a machine learning model capable of assessing the percentage cortex of each biopsy pass using smartphone images of the kidney biopsy tissue at the time of biopsy.

Methods

747 kidney biopsy cores and corresponding smartphone macro images were collected from five unused deceased donor kidneys. Each core was imaged, formalin-fixed, sectioned, and stained with Periodic acid-Schiff (PAS) to determine cortex percentage. The fresh unfixed core images were captured using the macro camera on an iPhone 13 Pro. Two experienced renal pathologists independently reviewed the PAS-stained sections to determine the cortex percentage. For the purpose of this study, the biopsies with less than 30% cortex were labeled as inadequate, while those with 30% or more cortex were classified as adequate. The dataset was divided into training (n=643), validation (n=30), and test (n=74) sets. Preprocessing steps involved converting High-Efficiency Image Container iPhone format images to JPEG, normalization, and renal tissue segmentation using a U-Net deep learning model. Subsequently, a classification deep learning model was trained on the renal tissue region of interest and corresponding class label.

Results

The deep learning model achieved an accuracy of 85% on the training data. On the independent test dataset, the model exhibited an accuracy of 81%. For inadequate samples in the test dataset, the model showed a sensitivity of 71%, suggesting its capability to identify cases with inadequate cortical representation. The area under the receiver-operating curve (AUC-ROC) on the test dataset was 0.80.

Conclusion

We successfully developed and tested a machine learning model for classifying smartphone images of kidney biopsies as either adequate or inadequate, based on the amount of cortex determined by expert renal pathologists. The model's promising results suggest its potential as a smartphone application to assist real-time assessment of kidney biopsy tissue, particularly in settings with limited access to trained personnel. Further refinements and validations are warranted to optimize the model's performance.

Genome-wide analysis reveals the contributors to fast molecular evolution of the Chinese hook snout carp (Opsariichthys bidens)

Variations in molecular evolutionary rate have been widely investigated among lineages and genes. However, it remains an open question whether fast rate of molecular evolution is driven by natural selection or random drift, and how the fast rate is linked to metabolic rate. Additionally, previous studies on fast molecular evolution have been largely restricted to concatenated matrix of genes or a few specifically selected genes, but less is known for individual genes at the genome-wide level. Here we addressed these questions using more than 5000 single-copy orthologous (SCO) genes through comparative genomic and phylogenetic analyses among fishes, with a special focus on a newly-sequenced clupeocephalan fish the Chinese hook snout carp Opsariichthys bidens. We showed O. bidens displays significantly higher mean substitution rate and more fast-evolving SCO genes (2172 genes) than most fishes studied here. The rapidly evolving genes are enriched in highly conserved and very basic functions such as translation and ribosome that are critical for biological fitness. We further revealed that ∼25 % of these fast-evolving genes exhibit a constant increase of substitution rate from the common ancestor down to the present, suggesting a neglected but important contribution from ancestral states. Model fitting showed that ∼85 % of fast-evolving genes exclusive to O. bidens and related species follow the adaptive evolutionary model rather than random-drift model, and 7.6 % of fast-evolving genes identified in O. bidens have experienced positive selection, both indicating the reflection of adaptive selection. Finally, metabolic rate was observed to be linked with substitution rate in a gene-specific manner. Overall, our findings reveal fast molecular evolution of SCO genes at genome-wide level in O. bidens, and uncover the evolutionary and ecological contributors to it.

Isomer-sourced structure iteration methods for in silico development of inhibitors: Inducing GTP-bound NRAS-Q61 oncogenic mutations to an "off-like" state

The NRAS-mutant subset of melanoma represent some of the most aggressive and deadliest types associated with poor overall survival. Unfortunately, for more than 40 years, no therapeutic agent directly targeting NRAS mutations has been clinically approved. In this work, based on microsecond scale molecular dynamics simulations, the effect of Q61 mutations on NRAS conformational characteristics is revealed at the atomic level. The GTP-bound NRAS-Q61R and Q61K mutations show a specific targetable pocket between Switch-II and α-helix 3 whereas the NRAS-Q61L non-polar mutation category shows a different targetable pocket. Moreover, a new isomer-sourced structure iteration method has been developed for the in silico design of potential inhibitor prototypes for oncogenes. We show the possibility of a designed prototype HM-387 to target activated NRAS-Q61R and that it can gradually induce the transition from the activated NRAS-Q61R to an "off-like" state.

Nuclear pore dysfunction and disease: a complex opportunity

The separation of genetic material from bulk cytoplasm has enabled the evolution of increasingly complex organisms, allowing for the development of sophisticated forms of life. However, this complexity has created new categories of dysfunction, including those related to the movement of material between cellular compartments. In eukaryotic cells, nucleocytoplasmic trafficking is a fundamental biological process, and cumulative disruptions to nuclear integrity and nucleocytoplasmic transport are detrimental to cell survival. This is particularly true in post-mitotic neurons, where nuclear pore injury and errors to nucleocytoplasmic trafficking are strongly associated with neurodegenerative disease. In this review, we summarize the current understanding of nuclear pore biology in physiological and pathological contexts and discuss potential therapeutic approaches for addressing nuclear pore injury and dysfunctional nucleocytoplasmic transport.

Human-specific epigenomic states in spermatogenesis

Infertility is becoming increasingly common, affecting one in six people globally. Half of these cases can be attributed to male factors, many driven by abnormalities in the process of sperm development. Emerging evidence from genome-wide association studies, genetic screening of patient cohorts, and animal models highlights an important genetic contribution to spermatogenic defects, but comprehensive identification and characterization of the genes critical for male fertility remain lacking. High divergence of gene regulation in spermatogenic cells across species poses challenges for delineating the genetic pathways required for human spermatogenesis using common model organisms. In this study, we leveraged post-translational histone modification and gene transcription data for 15,491 genes in four mammalian species (human, rhesus macaque, mouse, and opossum), to identify human-specific patterns of gene regulation during spermatogenesis. We combined H3K27me3 ChIP-seq, H3K4me3 ChIP-seq, and RNA-seq data to define epigenetic states for each gene at two stages of spermatogenesis, pachytene spermatocytes and round spermatids, in each species. We identified 239 genes that are uniquely active, poised, or dynamically regulated in human spermatogenic cells distinct from the other three species. While some of these genes have been implicated in reproductive functions, many more have not yet been associated with human infertility and may be candidates for further molecular and epidemiologic studies. Our analysis offers an example of the opportunities provided by evolutionary and epigenomic data for broadly screening candidate genes implicated in reproduction, which might lead to discoveries of novel genetic targets for diagnosis and management of male infertility and male contraception.

Optimal vaccination strategies on networks and in metropolitan areas

This study presents a mathematical model for optimal vaccination strategies in interconnected metropolitan areas, considering commuting patterns. It is a compartmental model with a vaccination rate for each city, acting as a control function. The commuting patterns are incorporated through a weighted adjacency matrix and a parameter that selects day and night periods. The optimal control problem is formulated to minimize a functional cost that balances the number of hospitalizations and vaccines, including restrictions of a weekly availability cap and an application capacity of vaccines per unit of time. The key findings of this work are bounds for the basic reproduction number, particularly in the case of a metropolitan area, and the study of the optimal control problem. Theoretical analysis and numerical simulations provide insights into disease dynamics and the effectiveness of control measures. The research highlights the importance of prioritizing vaccination in the capital to better control the disease spread, as we depicted in our numerical simulations. This model serves as a tool to improve resource allocation in epidemic control across metropolitan regions.

Transcription factor 3 is dysregulated in megakaryocytes in myelofibrosis

Transcription factor 3 (TCF3) is a DNA transcription factor that modulates megakaryocyte development. Although abnormal TCF3 expression has been identified in a range of hematological malignancies, to date, it has not been investigated in myelofibrosis (MF). MF is a Philadelphia-negative myeloproliferative neoplasm (MPN) that can arise de novo or progress from essential thrombocythemia [ET] and polycythemia vera [PV] and where dysfunctional megakaryocytes have a role in driving the fibrotic progression. We aimed to examine whether TCF3 is dysregulated in megakaryocytes in MPN, and specifically in MF. We first assessed TCF3 protein expression in megakaryocytes using an immunohistochemical approach analyses and showed that TCF3 was reduced in MF compared with ET and PV. Further, the TCF3-negative megakaryocytes were primarily located near trabecular bone and had the typical "MF-like" morphology as described by the WHO. Genomic analysis of isolated megakaryocytes showed three mutations, all predicted to result in a loss of function, in patients with MF; none were seen in megakaryocytes isolated from ET or PV marrow samples. We then progressed to transcriptomic sequencing of platelets which showed loss of TCF3 in MF. These proteomic, genomic and transcriptomic analyses appear to indicate that TCF3 is downregulated in megakaryocytes in MF. This infers aberrations in megakaryopoiesis occur in this progressive phase of MPN. Further exploration of this pathway could provide insights into TCF3 and the evolution of fibrosis and potentially lead to new preventative therapeutic targets.

T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors

Many pathogenic bacteria use type IV secretion systems (T4SSs) to deliver effectors (T4SEs) into the cytoplasm of eukaryotic cells, causing diseases. The identification of effectors is a crucial step in understanding the mechanisms of bacterial pathogenicity, but this remains a major challenge. In this study, we used the full-length embedding features generated by six pre-trained protein language models to train classifiers predicting T4SEs and compared their performance. We integrated three modules into a model called T4SEpp. The first module searched for full-length homologs of known T4SEs, signal sequences, and effector domains; the second module fine-tuned a machine learning model using data for a signal sequence feature; and the third module used the three best-performing pre-trained protein language models. T4SEpp outperformed other state-of-the-art (SOTA) software tools, achieving ∼0.98 accuracy at a high specificity of ∼0.99, based on the assessment of an independent validation dataset. T4SEpp predicted 13 T4SEs from Helicobacter pylori, including the well-known CagA and 12 other potential ones, among which eleven could potentially interact with human proteins. This suggests that these potential T4SEs may be associated with the pathogenicity of H. pylori. Overall, T4SEpp provides a better solution to assist in the identification of bacterial T4SEs and facilitates studies of bacterial pathogenicity. T4SEpp is freely accessible at https://bis.zju.edu.cn/T4SEpp.

Discovery and biocatalytic characterization of opine dehydrogenases by metagenome mining

Enzymatic processes play an increasing role in synthetic organic chemistry which requires the access to a broad and diverse set of enzymes. Metagenome mining is a valuable and efficient way to discover novel enzymes with unique properties for biotechnological applications. Here, we report the discovery and biocatalytic characterization of six novel metagenomic opine dehydrogenases from a hot spring environment (mODHs) (EC 1.5.1.X). These enzymes catalyze the asymmetric reductive amination between an amino acid and a keto acid resulting in opines which have defined biochemical roles and represent promising building blocks for pharmaceutical applications. The newly identified enzymes exhibit unique substrate specificity and higher thermostability compared to known examples. The feature that they preferably utilize negatively charged polar amino acids is so far unprecedented for opine dehydrogenases. We have identified two spatially correlated positions in their active sites that govern this substrate specificity and demonstrated a switch of substrate preference by site-directed mutagenesis. While they still suffer from a relatively narrow substrate scope, their enhanced thermostability and the orthogonality of their substrate preference make them a valuable addition to the toolbox of enzymes for reductive aminations. Importantly, enzymatic reductive aminations with highly polar amines are very rare in the literature. Thus, the preparative-scale enzymatic production, purification, and characterization of three highly functionalized chiral secondary amines lend a special significance to our work in filling this gap. KEY POINTS: • Six new opine dehydrogenases have been discovered from a hot spring metagenome • The newly identified enzymes display a unique substrate scope • Substrate specificity is governed by two correlated active-site residues.

Discovery of a novel marine Bacteroidetes with a rich repertoire of carbohydrate-active enzymes

Members of the phylum Bacteroidetes play a key role in the marine carbon cycle through their degradation of polysaccharides via carbohydrate-active enzymes (CAZymes) and polysaccharide utilization loci (PULs). The discovery of novel CAZymes and PULs is important for our understanding of the marine carbon cycle. In this study, we isolated and identified a potential new genus of the family Catalimonadaceae, in the phylum Bacteroidetes, from the southwest Indian Ocean. Strain TK19036, the type strain of the new genus, is predicted to encode CAZymes that are relatively abundant in marine Bacteroidetes genomes. Tunicatimonas pelagia NBRC 107804T, Porifericola rhodea NBRC 107748T and Catalinimonas niigatensis NBRC 109829T, which exhibit 16 S rRNA similarities exceeding 90% with strain TK19036, and belong to the same family, were selected as reference strains. These organisms possess a highly diverse repertoire of CAZymes and PULs, which may enable them to degrade a wide range of polysaccharides, especially pectin and alginate. In addition, some secretory CAZymes in strain TK19036 and its relatives were predicted to be transported by type IX secretion system (T9SS). Further, to the best of our knowledge, we propose the first reported "hybrid" PUL targeting alginates in T. pelagia NBRC 107804T. Our findings provide new insights into the polysaccharide degradation capacity of marine Bacteroidetes, and suggest that T9SS may play a more important role in this process than previously believed.

Laccase is a multitasking protein for synthetic gene circuits in the yeast Saccharomycescerevisiae

Laccase is a multicopper oxidase enzyme that oxidizes a variety of substrates, including polyphenols and polycyclic aromatic hydrocarbons (PAHs). It catalyzes the four-electron reduction of molecular oxygen that results in the production of water as a by-product. Thus, laccase can play an important role in environmental care. Previously, we have successfully expressed Trametes trogii laccase (TtLcc1) in the yeast Saccharomyces cerevisiae. In this work, we have expressed in yeast another laccase, LacA from Trametes sp. AH28-2, and tested its function on PAHs. Yeast cells engineered to produce the two laccases performed efficient PAH degradation. Both TtLcc1 and LacA led to the construction of spatiotemporal fluorescence-pulse generators when combined with a benzoate/salicylate yeast biosensor in a two-population system. Moreover, laccases returned a visual output signal in yeast synthetic circuits-upon reacting with ABTS (2,2'-azino-bis (3-ethylbenzthiazoline-6-sulfonic acid)). Thus, in S. cerevisiae, laccases are a powerful alternative to fluorescent reporter proteins.

Gene regulatory network analysis identifies MYL1, MDH2, GLS, and TRIM28 as the principal proteins in the response of mesenchymal stem cells to Mg2+ ions

Magnesium (Mg)-based implants have emerged as a promising alternative for orthopedic applications, owing to their bioactive properties and biodegradability. As the implants degrade, Mg2+ ions are released, influencing all surrounding cell types, especially mesenchymal stem cells (MSCs). MSCs are vital for bone tissue regeneration, therefore, it is essential to understand their molecular response to Mg2+ ions in order to maximize the potential of Mg-based biomaterials. In this study, we conducted a gene regulatory network (GRN) analysis to examine the molecular responses of MSCs to Mg2+ ions. We used time-series proteomics data collected at 11 time points across a 21-day period for the GRN construction. We studied the impact of Mg2+ ions on the resulting networks and identified the key proteins and protein interactions affected by the application of Mg2+ ions. Our analysis highlights MYL1, MDH2, GLS, and TRIM28 as the primary targets of Mg2+ ions in the response of MSCs during 1-21 days phase. Our results also identify MDH2-MYL1, MDH2-RPS26, TRIM28-AK1, TRIM28-SOD2, and GLS-AK1 as the critical protein relationships affected by Mg2+ ions. By offering a comprehensive understanding of the regulatory role of Mg2+ ions on MSCs, our study contributes valuable insights into the molecular response of MSCs to Mg-based materials, thereby facilitating the development of innovative therapeutic strategies for orthopedic applications.

Computational modeling for deciphering tissue microenvironment heterogeneity from spatially resolved transcriptomics

Spatial transcriptomics techniques, while measuring gene expression, retain spatial location information, aiding in situ studies of organismal tissue architecture and the progression of pathological processes. These techniques generate vast amounts of omics data, necessitating the development of computational methods to reveal the underlying tissue microenvironment heterogeneity. The main directions in spatial transcriptomics data analysis are spatial domain detection and spatial deconvolution, which can identify spatial functional regions and parse the distribution of cell types in spatial transcriptomics data by integrating single-cell transcriptomics data. In these two research directions, many computational methods have been successively proposed. This article will categorize them into three types: machine learning-based methods, probabilistic models-based methods, and deep learning-based methods. It will list and discuss the representative algorithms of each type along with their advantages and disadvantages and describe the datasets and evaluation metrics used to assess these computational methods, facilitating researchers in selecting suitable computational methods according to their research needs. Finally, combining the latest technological developments and the advantages and disadvantages of current algorithms, this article will look forward to the future directions of computational method development.

Analysis of Haemonchus embryos at single cell resolution identifies two eukaryotic elongation factors as intervention target candidates

Advances in single cell technologies are allowing investigations of a wide range of biological processes and pathways in animals, such as the multicellular model organism Caenorhabditis elegans - a free-living nematode. However, there has been limited application of such technology to related parasitic nematodes which cause major diseases of humans and animals worldwide. With no vaccines against the vast majority of parasitic nematodes and treatment failures due to drug resistance or inefficacy, new intervention targets are urgently needed, preferably informed by a deep understanding of these nematodes' cellular and molecular biology - which is presently lacking for most worms. Here, we created the first single cell atlas for an early developmental stage of Haemonchus contortus - a highly pathogenic, C. elegans-related parasitic nematode. We obtained and curated RNA sequence (snRNA-seq) data from single nuclei from embryonating eggs of H. contortus (150,000 droplets), and selected high-quality transcriptomic data for > 14,000 single nuclei for analysis, and identified 19 distinct clusters of cells. Guided by comparative analyses with C. elegans, we were able to reproducibly assign seven cell clusters to body wall muscle, hypodermis, neuronal, intestinal or seam cells, and identified eight genes that were transcribed in all cell clusters/types, three of which were inferred to be essential in H. contortus. Two of these genes (i.e. Hc-eef-1A and Hc-eef1G), coding for eukaryotic elongation factors (called Hc-eEF1A and Hc-eEF1G), were also demonstrated to be transcribed and expressed in all key developmental stages of H. contortus. Together with these findings, sequence- and structure-based comparative analyses indicated the potential of Hc-eEF1A and/or Hc-eEF1G as intervention targets within the protein biosynthesis machinery of H. contortus. Future work will focus on single cell studies of all key developmental stages and tissues of H. contortus, and on evaluating the suitability of the two elongation factor proteins as drug targets in H. contortus and related nematodes, with a view to finding new nematocidal drug candidates.

Network-based analysis of heterogeneous patient-matched brain and extracranial melanoma metastasis pairs reveals three homogeneous subgroups

Melanoma, the deadliest form of skin cancer, can metastasize to different organs. Molecular differences between brain and extracranial melanoma metastases are poorly understood. Here, promoter methylation and gene expression of 11 heterogeneous patient-matched pairs of brain and extracranial metastases were analyzed using melanoma-specific gene regulatory networks learned from public transcriptome and methylome data followed by network-based impact propagation of patient-specific alterations. This innovative data analysis strategy allowed to predict potential impacts of patient-specific driver candidate genes on other genes and pathways. The patient-matched metastasis pairs clustered into three robust subgroups with specific downstream targets with known roles in cancer, including melanoma (SG1: RBM38, BCL11B, SG2: GATA3, FES, SG3: SLAMF6, PYCARD). Patient subgroups and ranking of target gene candidates were confirmed in a validation cohort. Summarizing, computational network-based impact analyses of heterogeneous metastasis pairs predicted individual regulatory differences in melanoma brain metastases, cumulating into three consistent subgroups with specific downstream target genes.

Matrix-based project dataset parsers

There are several existing project datasets, which involve separate data sources for simulated and real projects, individual and multiprojects, and single- and multimodal attributes. In addition, their file structures are heterogeneous; therefore, scholars can usually use only one dataset to test a proposed scheduling or resource allocation algorithm. Since the internal structures of these projects are also very different, it is difficult to ensure that an algorithm optimized for a given type of project will also perform well on projects with other structures. The proposed parsing method supports researchers in:•reading several types of projects: simulated, real, individual, and multiprojects, as well as single- and multimodal attributes;•considering the priorities of activities and the flexibility of their dependencies, which is essential for modeling the structural flexibility employed by agile, hybrid, and extreme project management approaches;•building a large project database for testing and comparing different scheduling and resource allocation algorithms.

Isolation of community-associated methicillin-resistant Staphylococcus aureus (MRSA) sequence type (ST) 30 from house rats (Rattus tanezumi) in Hong Kong

Methicillin-resistant Staphylococcus aureus (MRSA) is of major public health concern due to its resistance to multiple antibiotics. This resistance has been observed in various settings, including hospitals and communities, and has been detected in both animals and humans. Although peridomestic rat species (Rattus spp.) are well described reservoirs of several human pathogens and antimicrobial resistant bacteria, little is known about their role in MRSA epidemiology. In order to investigate whether Rattus spp. in Hong Kong are potential carriers of MRSA, 221 rats were caught from various ecological areas and nasopharyngeal samples were cultured on MRSA selective media. Genotypic characteristics of MRSA were confirmed by whole genome sequencing. Two clonal sequence type (ST) 30 MRSA isolates, harbouring mecA on staphylococcal chromosome cassette (SCC) mec type IVc, were cultured from two house rats (Rattus tanezumi) caught in two densely populated urban areas. To the best of the authors' knowledge, this is the first detection of community-associated (CA)-MRSA strain ST30 SCCmec IVc in peridomestic rodents in Hong Kong and globally. Our finding indicates that house rats can be carriers of MRSA strains that are widely distributed in the community.

Enhancing prediction of short linear protein motifs with Wregex 3.0

Short linear motifs (SLiMs) play an important role in protein-protein interactions. However, SLiM patterns are intrinsically permissive and result into many matches that occur just by chance, specially when targeting large datasets. To prioritize these matches as candidates for functional testing, we developed Wregex (Weighted regular expression), which uses a position-specific scoring matrix (PSSM) to order a list of regular expression matches according to a PSSM-derived score. Here we present Wregex 3.0, an improved version with new functionalities such as the support for a second auxiliary motif to help refining prediction of a primary SLiM, and post-translational modifications (PTMs) enrichment taking into account that many regulatory SLiM-mediated interactions are modulated by one or more PTMs. This version also incorporates a number of new features such as a convenient use of subproteomes, showing UniProt annotations such as disordered regions, searching for all known motifs and generating decoy databases for enrichment analysis. We provide case studies to illustrate how these new Wregex functionalities enhance prediction of short linear protein motifs. The Wregex 3.0 server is freely accessible at https://ehubio.ehu.eus/wregex3/.

Genomic context determines the effect of DNA methylation on gene expression in the gut epithelium of Atlantic salmon (Salmo salar)

The canonical view of DNA methylation, a pivotal epigenetic regulation mechanism in eukaryotes, dictates its role as a suppressor of gene activity, particularly within promoter regions. However, this view is being challenged as it is becoming increasingly evident that the connection between DNA methylation and gene expression varies depending on the genomic location and is therefore more complex than initially thought. We examined DNA methylation levels in the gut epithelium of Atlantic salmon (Salmo salar) using whole-genome bisulfite sequencing, which we correlated with gene expression data from RNA sequencing of the same gut tissue sample (RNA-seq). Assuming epigenetic signals might be pronounced between distinctive phenotypes, we compared large and small fish, finding 22 significant associations between 22 differentially methylated regions and 21 genes. We did not detect significant methylation differences between large and small fish. However, we observed a consistent signal of methylation levels around the transcription start sites (TSS), being negatively correlated with the expression levels of those genes. We found both negative and positive associations of methylation levels with gene expression further upstream or downstream of the TSS, revealing a more unpredictable pattern. The 21 genes showing significant methylation-expression correlations were involved in biological processes related to salmon health, such as growth and immune responses. Deciphering how DNA methylation affects the expression of such genes holds great potential for future applications. For instance, our results suggest the importance of genomic context in targeting epigenetic modifications to improve the welfare of aquaculture species like Atlantic salmon.

A multidomain PARP14 construct suitable for bacterial expression

Poly-ADP-ribose polymerase-14 (PARP14) can modify proteins and nucleic acids by the reversible addition of a single ADP-ribose molecule. Aberrant PARP14 functions have been related to cancer and inflammation, and its domains are involved in processes related to viral infection. Previous research indicates that PARP14 functions might be mediated via a multitude of target proteins. In vitro studies of this large multidomain enzyme have been complicated by difficulties to obtain biochemical quantities of pure protein. Here we present a strategy that allows bacterial expression and purification of a functional multidomain construct of PARP14. We substituted an internal KH domain and its neighboring unstructured region with a SUMO domain to obtain a protein construct that encompasses three macrodomains, a WWE domain, and a PARP catalytic domain. We show that the resulting construct retains both ADP-ribosyltransferase and de-MARylase activities. This construct will be useful in structural and functional studies of PARP14.

Influences of sleep, cortisol reactivity, and risk/reward-based decision-making on suicide

BACKGROUND AND OBJECTIVES

Poor sleep quality is a known contributor to suicidal thoughts and behaviors. This study examines whether sleep quality modulates the effect of an individual's stress response and risk/reward-based decision making on suicide risk.

METHODS

Participants were 160 adults at a residential substance use treatment facility with lifetime exposure to trauma who completed a clinician-administered measure of suicide risk, the Iowa Gambling Task (IGT), and a self-report measure of sleep. Cortisol reactivity (i.e., changes in cortisol before and after a personalized trauma script) was used to measure stress response. We used quantile regression to examine the effects of sleep, cortisol, and risk/reward decision-making on suicide risk.

RESULTS

We found poor sleep quality to be increasingly salient in individuals at greater risk for suicide than those at lower risk for suicide. Furthermore, individuals with moderate to moderate-high levels of suicide risk seem to have greater cortisol reactivity. In the low-moderate quantile, we found suicide risk to be associated with both high stress reactivity and low-risk, high-reward decision-making, as well as low stress reactivity and high-risk/low-reward decision-making.

LIMITATIONS

These findings should be interpreted considering several methodological constraints, such as the use of a pre-determined sample and instruments not tailored for our hypotheses, the MINI 'Suicide' Module's limited differentiation between suicidal ideation and behavior, and variably timed cortisol sampling.

CONCLUSIONS

Despite these limitations, the findings from this study support the use of evidence-based interventions focused on improving sleep quality and managing emotional reactivity to decrease suicide risk.

Computational methods for alignment and integration of spatially resolved transcriptomics data

Most of the complex biological regulatory activities occur in three dimensions (3D). To better analyze biological processes, it is essential not only to decipher the molecular information of numerous cells but also to understand how their spatial contexts influence their behavior. With the development of spatially resolved transcriptomics (SRT) technologies, SRT datasets are being generated to simultaneously characterize gene expression and spatial arrangement information within tissues, organs or organisms. To fully leverage spatial information, the focus extends beyond individual two-dimensional (2D) slices. Two tasks known as slices alignment and data integration have been introduced to establish correlations between multiple slices, enhancing the effectiveness of downstream tasks. Currently, numerous related methods have been developed. In this review, we first elucidate the details and principles behind several representative methods. Then we report the testing results of these methods on various SRT datasets, and assess their performance in representative downstream tasks. Insights into the strengths and weaknesses of each method and the reasons behind their performance are discussed. Finally, we provide an outlook on future developments. The codes and details of experiments are now publicly available at https://github.com/YangLabHKUST/SRT_alignment_and_integration.

drexml: A command line tool and Python package for drug repurposing

We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions. In the Fanconi Anemia case, the model successfully predicts previously validated repurposed drugs, while in the Familial Melanoma case, it identifies a promising set of drugs for further investigation.

Community composition of phytopathogenic fungi significantly influences ectomycorrhizal fungal communities during subtropical forest succession

Ectomycorrhizal fungi (EMF) can form symbiotic relationships with plants, aiding in plant growth by providing access to nutrients and defense against phytopathogenic fungi. In this context, factors such as plant assemblages and soil properties can impact the interaction between EMF and phytopathogenic fungi in forest soil. However, there is little understanding of how these fungal interactions evolve as forests move through succession stages. In this study, we used high-throughput sequencing to investigate fungal communities in young, intermediate, and old subtropical forests. At the genus level, EMF communities were dominated by Sebacina, Russula, and Lactarius, while Mycena was the most abundant genus in pathogenic fungal communities. The relative abundances of EMF and phytopathogenic fungi in different stages showed no significant difference with the regulation of different factors. We discovered that interactions between phytopathogenic fungi and EMF maintained a dynamic balance under the influence of the differences in soil quality attributed to each forest successional stage. The community composition of phytopathogenic fungi is one of the strong drivers in shaping EMF communities over successions. In addition, the EMF diversity was significantly related to plant diversity, and these relationships varied among successional stages. Despite the regulation of various factors, the positive relationship between the diversity of phytopathogenic fungi and EMF remained unchanged. However, there is no significant difference in the ratio of the abundance of EMF and phytopathogenic fungi over the course of successions. These results will advance our understanding of the biodiversity-ecosystem functioning during forest succession. KEY POINTS: •Community composition of both EMF and phytopathogenic fungi changed significantly over forest succession. •Phytopathogenic fungi is a key driver in shaping EMF community. •The effect of plant Shannon's diversity on EMF communities changed during the forest aging process.

Inhibitory effect of truncated isoforms on GPCR dimerization predicted by combinatorial computational strategy

G protein-coupled receptors (GPCRs) play a pivotal role in fundamental biological processes and disease development. GPCR isoforms, derived from alternative splicing, can exhibit distinct signaling patterns. Some highly-truncated isoforms can impact functional performance of full-length receptors, suggesting their intriguing regulatory roles. However, how these truncated isoforms interact with full-length counterparts remains largely unexplored. Here, we computationally investigated the interaction patterns of three human GPCRs from three different classes, ADORA1 (Class A), mGlu2 (Class C) and SMO (Class F) with their respective truncated isoforms because their homodimer structures have been experimentally determined, and they have truncated isoforms deposited and identified at protein level in Uniprot database. Combining the neural network-based AlphaFold2 and two physics-based protein-protein docking tools, we generated multiple complex structures and assessed the binding affinity in the context of atomistic molecular dynamics simulations. Our computational results suggested all the four studied truncated isoforms showed potent binding to their counterparts and overlapping interfaces with homodimers, indicating their strong potential to block homodimerization of their counterparts. Our study offers insights into functional significance of GPCR truncated isoforms and supports the ubiquity of their regulatory roles.

RfxCas13d-mediated inhibition of Circ1647 alleviates renal fibrosis via PI3K/AKT signaling pathway

BACKGROUND

Circular RNAs (CircRNAs) have been shown to be involved in the development of chronic kidney disease (CKD). This study aimed to investigate the role of Circ1647 in renal fibrosis, which is a hallmark of CKD.

METHODS

In this study, we established a unilateral ureteral obstruction (UUO) model and delivered Circ1647 RfxCas13d knockdown plasmid into renal parenchymal cells via retrograde injection through the ureter followed by electroporation. After that, the pathological changes were determined by Hematoxylin and Eosin. Meanwhile, Immunohistochemistry, qRT-PCR and Western blot were conducted to assess the degree of fibrosis. In addition, overexpressing of Circ1647 in renal tubular epithelial cells (TCMK1) was performed to investigate the underlying mechanisms of Circ1647.

RESULTS

Our results displayed that electroporation-mediated knockdown of Circ1647 by RfxCas13d knockdown plasmid significantly inhibited renal fibrosis in UUO mice as evidenced by reduced expression of fibronectin and α-SMA (alpha-smooth muscle actin). Conversely, overexpression of Circ1647 in TCMK1 cells promoted the fibrosis. In terms of mechanism, Circ1647 may mediate the PI3K/AKT Signaling Pathway as demonstrated by the balance of the phosphorylation of PI3K and AKT in vivo and the aggravated phosphorylation of PI3K and AKT in vitro. These observations were corroborated by the effects of the PI3K inhibitor LY294002, which mitigated fibrosis post Circ1647 overexpression.

CONCLUSION

Our study suggests that Circ1647 plays a significant role in renal fibrosis by mediating the PI3K/AKT signaling pathway. RfxCas13d-mediated inhibition of Circ1647 may serve as a therapeutic target for renal fibrosis in CKD.

Deep Learning of radiology-genomics integration for computational oncology: A mini review

In the field of computational oncology, patient status is often assessed using radiology-genomics, which includes two key technologies and data, such as radiology and genomics. Recent advances in deep learning have facilitated the integration of radiology-genomics data, and even new omics data, significantly improving the robustness and accuracy of clinical predictions. These factors are driving artificial intelligence (AI) closer to practical clinical applications. In particular, deep learning models are crucial in identifying new radiology-genomics biomarkers and therapeutic targets, supported by explainable AI (xAI) methods. This review focuses on recent developments in deep learning for radiology-genomics integration, highlights current challenges, and outlines some research directions for multimodal integration and biomarker discovery of radiology-genomics or radiology-omics that are urgently needed in computational oncology.

Discovering novel Cathepsin L inhibitors from natural products using artificial intelligence

Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders. Current pharmacological interventions targeting CTSL have demonstrated potential in reducing body weight gain, serum insulin levels, and improving glucose tolerance. However, the clinical application of CTSL inhibitors remains limited. In this study, we used a combination of artificial intelligence and experimental methods to identify new CTSL inhibitors from natural products. Through a robust deep learning model and molecular docking, we screened 150 molecules from natural products for experimental validation. At a concentration of 100 µM, we found that 36 of them exhibited more than 50 % inhibition of CTSL. Notably, 13 molecules displayed over 90 % inhibition and exhibiting concentration-dependent effects. The molecular dynamics simulation on the two most potent inhibitors, Plumbagin and Beta-Lapachone, demonstrated stable interaction at the CTSL active site. Enzyme kinetics studies have shown that these inhibitors exert an uncompetitive inhibitory effect on CTSL. In conclusion, our research identifies Plumbagin and Beta-Lapachone as potential CTSL inhibitors, offering promising candidates for the treatment of metabolic disorders and illustrating the effectiveness of artificial intelligence in drug discovery.

Accurate size-based protein localization from cryo-ET tomograms

Cryo-electron tomography (cryo-ET) combined with sub-tomogram averaging (STA) allows the determination of protein structures imaged within the native context of the cell at near-atomic resolution. Particle picking is an essential step in the cryo-ET/STA image analysis pipeline that consists in locating the position of proteins within crowded cellular tomograms so that they can be aligned and averaged in 3D to improve resolution. While extensive work in 2D particle picking has been done in the context of single-particle cryo-EM, comparatively fewer strategies have been proposed to pick particles from 3D tomograms, in part due to the challenges associated with working with noisy 3D volumes affected by the missing wedge. While strategies based on 3D template-matching and deep learning are commonly used, these methods are computationally expensive and require either an external template or manual labelling which can bias the results and limit their applicability. Here, we propose a size-based method to pick particles from tomograms that is fast, accurate, and does not require external templates or user provided labels. We compare the performance of our approach against a commonly used algorithm based on deep learning, crYOLO, and show that our method: i) has higher detection accuracy, ii) does not require user input for labeling or time-consuming training, and iii) runs efficiently on non-specialized CPU hardware. We demonstrate the effectiveness of our approach by automatically detecting particles from tomograms representing different types of samples and using these particles to determine the high-resolution structures of ribosomes imaged in vitro and in situ.

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Transcriptomic investigations of polymyxins and colistin/sulbactam combination against carbapenem-resistant Acinetobacter baumannii

Carbapenem-resistant Acinetobacter baumannii (CRAB) is a Priority 1 (Critical) pathogen urgently requiring new antibiotics. Polymyxins are a last-line option against CRAB-associated infections. This transcriptomic study utilized a CRAB strain to investigate mechanisms of bacterial killing with polymyxin B, colistin, colistin B, and colistin/sulbactam combination therapy. After 4 h of 2 mg/L polymyxin monotherapy, all polymyxins exhibited common transcriptomic responses which primarily involved disruption to amino acid and fatty acid metabolism. Of the three monotherapies, polymyxin B induced the greatest number of differentially expressed genes (DEGs), including for genes involved with fatty acid metabolism. Gene disturbances with colistin and colistin B were highly similar (89 % common genes for colistin B), though effects on gene expression were generally lower (0-1.5-fold in most cases) with colistin B. Colistin alone (2 mg/L) or combined with sulbactam (64 mg/L) resulted in rapid membrane disruption as early as 1 h. Transcriptomic analysis of this combination revealed that the effects were driven by colistin, which included disturbances in fatty acid synthesis and catabolism, and inhibition of nutrient uptake. Combination therapy produced substantially higher fold changes in 72 % of DEGs shared with monotherapy, leading to substantially greater reductions in fatty acid biosynthesis and increases in biofilm, cell wall, and phospholipid synthesis. This indicates synergistic bacterial killing with the colistin/sulbactam combination results from a systematic increase in perturbation of many genes associated with bacterial metabolism. These mechanistic insights enhance our understanding of bacterial responses to polymyxin mono- and combination therapy and will assist to optimize polymyxin use in patients.

Comparison of stromal vascular fraction cell composition between Coleman fat and extracellular matrix/stromal vascular fraction gel

As a mechanically condensed product of Coleman fat, extracellular matrix/stromal vascular fraction gel (ECM/SVF-gel) eliminates adipocytes, concentrates SVF cells, and improves fat graft retention. This study aims to compare SVF cell composition between Coleman fat and ECM/SVF-gel. Matched Coleman fat and ECM/SVF-gel of 28 healthy women were subjected to RNA-seq, followed by functional enrichment and cell-type-specific enrichment analyses, and deconvolution of SVF cell subsets, reconstructing SVF cell composition in the transcriptome level. ECM/SVF-gels had 9 upregulated and 73 downregulated differentially expressed genes (DEGs). Downregulated DEGs were mainly associated with inflammatory and immune responses, and enriched in fat macrophages. M2 macrophages, resting CD4+ memory T cells, M1 macrophages, resting mast cells, and M0 macrophages ranked in the top five most prevalent immune cells in the two groups. The proportions of the principal non-immune cells (e.g., adipose-derived stem cells, pericytes, preadipocytes, microvascular endothelial cells) had no statistical differences between the two groups. Our findings reveal ECM/SVF-gels share the same dominant immune cells beneficial to fat graft survival with Coleman fat, but exhibiting obvious losses of immune cells (especially macrophages), while non-immune cells necessary for adipose regeneration might have no significant loss in ECM/SVF-gels and their biological effects could be markedly enhanced by the ECM/SVF-gel's condensed nature.

Classification of bioactive peptides: A systematic benchmark of models and encodings

Bioactive peptides are short amino acid chains possessing biological activity and exerting physiological effects relevant to human health. Despite their therapeutic value, their identification remains a major problem, as it mainly relies on time-consuming in vitro tests. While bioinformatic tools for the identification of bioactive peptides are available, they are focused on specific functional classes and have not been systematically tested on realistic settings. To tackle this problem, bioactive peptide sequences and functions were here gathered from a variety of databases to generate a unified collection of bioactive peptides from microbial fermentation. This collection was organized into nine functional classes including some previously studied and some unexplored such as immunomodulatory, opioid and cardiovascular peptides. Upon assessing their sequence properties, four alternative encoding methods were tested in combination with a multitude of machine learning algorithms, from basic classifiers like logistic regression to advanced algorithms like BERT. Tests on a total of 171 models showed that, while some functions are intrinsically easier to detect, no single combination of classifiers and encoders worked universally well for all classes. For this reason, we unified all the best individual models for each class and generated CICERON (Classification of bIoaCtive pEptides fRom micrObial fermeNtation), a classification tool for the functional classification of peptides. State-of-the-art classifiers were found to underperform on our realistic benchmark dataset compared to the models included in CICERON. Altogether, our work provides a tool for real-world peptide classification and can serve as a benchmark for future model development.

Single-cell 5' RNA sequencing of camelid peripheral B cells provides insights into cellular basis of heavy-chain antibody production

Camelids produce both conventional tetrameric antibodies (Abs) and dimeric heavy-chain antibodies (HCAbs). Although B cells that generate these two types of Abs exhibit distinct B cell receptors (BCRs), whether these two B cell populations differ in their phenotypes and developmental processes remains unclear. Here, we performed single-cell 5' RNA profiling of peripheral blood mononuclear cell samples from Bactrian camels before and after immunization. We characterized the functional subtypes and differentiation trajectories of circulating B cells in camels, and reconstructed single-cell BCR sequences. We found that in contrast to humans, the proportion of T-bet+ B cells was high among camelid peripheral B cells. Several marker genes of human B cell subtypes, including CD27 and IGHD, were expressed at low levels in the corresponding camel B cell subtypes. Camelid B cells expressing variable genes of HACbs (VHH) were widely present in various functional subtypes and showed highly overlapping differentiation trajectories with B cells expressing variable genes of conventional Abs (VH). After immunization, the transcriptional changes in VHH+ and VH+ B cells were largely consistent. Through structure modeling, we identified a variety of scaffold types among the reconstructed VHH sequences. Our study provides insights into the cellular context of HCAb production in camels and lays the foundation for developing single-B cell-based camelid single-domain Ab screening.