Functional and structural insights into RAS effector proteins

RAS proteins are conserved guanosine triphosphate (GTP) hydrolases (GTPases) that act as molecular binary switches and play vital roles in numerous cellular processes. Upon GTP binding, RAS GTPases adopt an active conformation and interact with specific proteins termed RAS effectors that contain a conserved ubiquitin-like domain, thereby facilitating downstream signaling. Over 50 effector proteins have been identified in the human proteome, and many have been studied as potential mediators of RAS-dependent signaling pathways. Biochemical and structural analyses have provided mechanistic insights into these effectors, and studies using model organisms have complemented our understanding of their role in physiology and disease. Yet, many critical aspects regarding the dynamics and biological function of RAS-effector complexes remain to be elucidated. In this review, we discuss the mechanisms and functions of known RAS effector proteins, provide structural perspectives on RAS-effector interactions, evaluate their significance in RAS-mediated signaling, and explore their potential as therapeutic targets.

Integrated ML-Based Strategy Identifies Drug Repurposing for Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) affects an estimated global population of around 3 million individuals. IPF is a medical condition with an unknown cause characterized by the formation of scar tissue in the lungs, leading to progressive respiratory disease. Currently, there are only two FDA-approved small molecule drugs specifically for the treatment of IPF and this has created a demand for the rapid development of drugs for IPF treatment. Moreover, denovo drug development is time and cost-intensive with less than a 10% success rate. Drug repurposing currently is the most feasible option for rapidly making the drugs to market for a rare and sporadic disease. Normally, the repurposing of drugs begins with a screening of FDA-approved drugs using computational tools, which results in a low hit rate. Here, an integrated machine learning-based drug repurposing strategy is developed to significantly reduce the false positive outcomes by introducing the predock machine-learning-based predictions followed by literature and GSEA-assisted validation and drug pathway prediction. The developed strategy is deployed to 1480 FDA-approved drugs and to drugs currently in a clinical trial for IPF to screen them against "TGFB1", "TGFB2", "PDGFR-a", "SMAD-2/3", "FGF-2", and more proteins resulting in 247 total and 27 potentially repurposable drugs. The literature and GSEA validation suggested that 72 of 247 (29.14%) drugs have been tried for IPF, 13 of 247 (5.2%) drugs have already been used for lung fibrosis, and 20 of 247 (8%) drugs have been tested for other fibrotic conditions such as cystic fibrosis and renal fibrosis. Pathway prediction of the remaining 142 drugs was carried out resulting in 118 distinct pathways. Furthermore, the analysis revealed that 29 of 118 pathways were directly or indirectly involved in IPF and 11 of 29 pathways were directly involved. Moreover, 15 potential drug combinations are suggested for showing a strong synergistic effect in IPF. The drug repurposing strategy reported here will be useful for rapidly developing drugs for treating IPF and other related conditions.

Computational tools to predict context-specific protein complexes

Interactions between thousands of proteins define cells' protein-protein interaction (PPI) network. Some of these interactions lead to the formation of protein complexes. It is challenging to identify a protein complex in a haystack of protein-protein interactions, and it is even more difficult to predict all protein complexes of the complexome. Simulations and machine learning approaches try to crack these problems by looking at the PPI network or predicted protein structures. Clustering of PPI networks led to the first protein complex predictions, while most recently, atomistic models of protein complexes and deep-learning-based structure prediction methods have also emerged. The simulation of PPI level interactions even enables the quantitative prediction of protein complexes. These methods, the required data sources, and their potential future developments are discussed in this review.

Predicting blood-brain barrier permeability of molecules with a large language model and machine learning

Predicting the blood-brain barrier (BBB) permeability of small-molecule compounds using a novel artificial intelligence platform is necessary for drug discovery. Machine learning and a large language model on artificial intelligence (AI) tools improve the accuracy and shorten the time for new drug development. The primary goal of this research is to develop artificial intelligence (AI) computing models and novel deep learning architectures capable of predicting whether molecules can permeate the human blood-brain barrier (BBB). The in silico (computational) and in vitro (experimental) results were validated by the Natural Products Research Laboratories (NPRL) at China Medical University Hospital (CMUH). The transformer-based MegaMolBART was used as the simplified molecular input line entry system (SMILES) encoder with an XGBoost classifier as an in silico method to check if a molecule could cross through the BBB. We used Morgan or Circular fingerprints to apply the Morgan algorithm to a set of atomic invariants as a baseline encoder also with an XGBoost classifier to compare the results. BBB permeability was assessed in vitro using three-dimensional (3D) human BBB spheroids (human brain microvascular endothelial cells, brain vascular pericytes, and astrocytes). Using multiple BBB databases, the results of the final in silico transformer and XGBoost model achieved an area under the receiver operating characteristic curve of 0.88 on the held-out test dataset. Temozolomide (TMZ) and 21 randomly selected BBB permeable compounds (Pred scores = 1, indicating BBB-permeable) from the NPRL penetrated human BBB spheroid cells. No evidence suggests that ferulic acid or five BBB-impermeable compounds (Pred scores < 1.29423E-05, which designate compounds that pass through the human BBB) can pass through the spheroid cells of the BBB. Our validation of in vitro experiments indicated that the in silico prediction of small-molecule permeation in the BBB model is accurate. Transformer-based models like MegaMolBART, leveraging the SMILES representations of molecules, show great promise for applications in new drug discovery. These models have the potential to accelerate the development of novel targeted treatments for disorders of the central nervous system.

Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach

Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.

Modular Control of Biological Networks

The concept of control is central to understanding and applications of biological network models. Some of their key structural features relate to control functions, through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications of models often focus on model-based control, such as in biomedicine or metabolic engineering. This paper presents an approach to model-based control that exploits two common features of biological networks, namely their modular structure and canalizing features of their regulatory mechanisms. The paper focuses on intracellular regulatory networks, represented by Boolean network models. A main result of this paper is that control strategies can be identified by focusing on one module at a time. This paper also presents a criterion based on canalizing features of the regulatory rules to identify modules that do not contribute to network control and can be excluded. For even moderately sized networks, finding global control inputs is computationally very challenging. The modular approach presented here leads to a highly efficient approach to solving this problem. This approach is applied to a published Boolean network model of blood cancer large granular lymphocyte (T-LGL) leukemia to identify a minimal control set that achieves a desired control objective.

Integrative genomic reconstruction of carbohydrate utilization networks in bifidobacteria: global trends, local variability, and dietary adaptation

Bifidobacteria are among the earliest colonizers of the human gut, conferring numerous health benefits. While multiple Bifidobacterium strains are used as probiotics, accumulating evidence suggests that the individual responses to probiotic supplementation may vary, likely due to a variety of factors, including strain type(s), gut community composition, dietary habits of the consumer, and other health/lifestyle conditions. Given the saccharolytic nature of bifidobacteria, the carbohydrate composition of the diet is one of the primary factors dictating the colonization efficiency of Bifidobacterium strains. Therefore, a comprehensive understanding of bifidobacterial glycan metabolism at the strain level is necessary to rationally design probiotic or synbiotic formulations that combine bacterial strains with glycans that match their nutrient preferences. In this study, we systematically reconstructed 66 pathways involved in the utilization of mono-, di-, oligo-, and polysaccharides by analyzing the representation of 565 curated metabolic functional roles (catabolic enzymes, transporters, transcriptional regulators) in 2973 non-redundant cultured Bifidobacterium isolates and metagenome-assembled genomes (MAGs). Our analysis uncovered substantial heterogeneity in the predicted glycan utilization capabilities at the species and strain level and revealed the presence of a yet undescribed phenotypically distinct subspecies-level clade within the Bifidobacterium longum species. We also identified Bangladeshi isolates harboring unique gene clusters tentatively implicated in the breakdown of xyloglucan and human milk oligosaccharides. Predicted carbohydrate utilization phenotypes were experimentally characterized and validated. Our large-scale genomic analysis considerably expands the knowledge of carbohydrate metabolism in bifidobacteria and provides a foundation for rationally designing single- or multi-strain probiotic formulations of a given bifidobacterial species as well as synbiotic combinations of bifidobacterial strains matched with their preferred carbohydrate substrates.

Subregion-specific transcriptomic profiling of rat brain reveals sex-distinct gene expression impacted by adolescent stress

Stress during adolescence clearly impacts brain development and function. Sex differences in adolescent stress-induced or exacerbated emotional and metabolic vulnerabilities could be due to sex-distinct gene expression in hypothalamic, limbic, and prefrontal brain regions. However, adolescent stress-induced whole-genome expression changes in key subregions of these brain regions were unclear. In this study, female and male adolescent Sprague Dawley rats received one-hour restraint stress daily from postnatal day (PD) 32 to PD44. Corticosterone levels, body weights, food intake, body composition, and circulating adiposity and sex hormones were measured. On PD44, brain and blood samples were collected. Using RNA-sequencing, sex-specific differences in stress-induced differentially expressed (DE) genes were identified in subregions of the hypothalamus, limbic system, and prefrontal cortex. Canonical pathways reflected well-known sex-distinct maladies and diseases, substantiating the therapeutic potential of the DE genes found in the current study. Thus, we proposed specific sex distinct, adolescent stress-induced transcriptional changes found in the current study as examples of the molecular bases for sex differences witnessed in stress induced or exacerbated emotional and metabolic disorders. Future behavioral studies and single-cell studies are warranted to test the implications of the DE genes identified in this study in sex-distinct stress-induced susceptibilities.

A decade of thermostatted kinetic theory models for complex active matter living systems

In the last decade, the thermostatted kinetic theory has been proposed as a general paradigm for the modeling of complex systems of the active matter and, in particular, in biology. Homogeneous and inhomogeneous frameworks of the thermostatted kinetic theory have been employed for modeling phenomena that are the result of interactions among the elements, called active particles, composing the system. Functional subsystems contain heterogeneous active particles that are able to perform the same task, called activity. Active matter living systems usually operate out-of-equilibrium; accordingly, a mathematical thermostat is introduced in order to regulate the fluctuations of the activity of particles. The time evolution of the functional subsystems is obtained by introducing the conservative and the nonconservative interactions which represent activity-transition, natural birth/death, induced proliferation/destruction, and mutation of the active particles. This review paper is divided in two parts: In the first part the review deals with the mathematical frameworks of the thermostatted kinetic theory that can be found in the literature of the last decade and a unified approach is proposed; the second part of the review is devoted to the specific mathematical models derived within the thermostatted kinetic theory presented in the last decade for complex biological systems, such as wound healing diseases, the recognition process and the learning dynamics of the human immune system, the hiding-learning dynamics and the immunoediting process occurring during the cancer-immune system competition. Future research perspectives are discussed from the theoretical and application viewpoints, which suggest the important interplay among the different scholars of the applied sciences and the desire of a multidisciplinary approach or rather a theory for the modeling of every active matter system.

Inhibitors of the tyrosine kinases FMS-like tyrosine kinase-3 and WEE1 induce apoptosis and DNA damage synergistically in acute myeloid leukemia cells

Hyperactive FMS-like receptor tyrosine kinase-3 mutants with internal tandem duplications (FLT3-ITD) are frequent driver mutations of aggressive acute myeloid leukemia (AML). Inhibitors of FLT3 produce promising results in rationally designed cotreatment schemes. Since FLT3-ITD modulates DNA replication and DNA repair, valid anti-leukemia strategies could rely on a combined inhibition of FLT3-ITD and regulators of cell cycle progression and DNA integrity. These include the WEE1 kinase which controls cell cycle progression, nucleotide synthesis, and DNA replication origin firing. We investigated how pharmacological inhibition of FLT3 and WEE1 affected the survival and genomic integrity of AML cell lines and primary AML cells. We reveal that promising clinical grade and preclinical inhibitors of FLT3 and WEE1 synergistically trigger apoptosis in leukemic cells that express FLT3-ITD. An accumulation of single and double strand DNA damage precedes this process. Mass spectrometry-based proteomic analyses show that FLT3-ITD and WEE1 sustain the expression of the ribonucleotide reductase subunit RRM2, which provides dNTPs for DNA replication. Unlike their strong pro-apoptotic effects on leukemia cells with FLT3-ITD, inhibitors of FLT3 and WEE1 do not damage healthy human blood cells and murine hematopoietic stem cells. Thus, pharmacological inhibition of FLT3-ITD and WEE1 might become an improved, rationally designed therapeutic option.

A comprehensive review of computational cell cycle models in guiding cancer treatment strategies

This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.

Antiviral effect of alkaloids-free Ephedra Herb extract on respiratory syncytial virus infection

Respiratory syncytial virus (RSV) is a major cause of respiratory tract infection in children. Despite decades of efforts, no effective therapies are available. We recently reported that extracts of Ephedra Herb and Cinnamon Bark interacted with the G attachment protein of RSV to inhibit infectivity. The present in vitro study aimed to investigate the antiviral effect of ephedrine alkaloids-free Ephedra Herb extract (EFE), which is characterized by free of harmful effects of ephedrine alkaloids in Ephedra Herb, on experimental RSV infection. Infection of RSV into A549 cells simultaneously with EFE resulted the significant reduction of RSV RNA, viral protein, and viral titers after the incubation of the cells. We found that RSV attachment to the cell surface was inhibited both in the presence of EFE and when RSV particles were pre-treated with EFE. We also found that EFE specifically interacted with the central conserved domain of RSV G protein by surface plasmon resonance, demonstrating that specific binding of G protein to the cellular receptor was inhibited by EFE. Another mechanism was found in which a higher concentration of EFE inhibited the viral load immediately after the viral entry into host cells, suggesting the inhibition of viral RNA replication. These results demonstrate that EFE worked against RSV infection through multiple antiviral mechanisms, a unique feature of this crude drug extract.

Multiomics to Characterize the Molecular Events Underlying Impaired Glucose Tolerance in FXR-Knockout Mice

The prevalence of different metabolic syndromes has grown globally, and the farnesoid X receptor (FXR), a metabolic homeostat for glucose, lipid, and bile acid metabolisms, may serve an important role in the progression of metabolic disorders. Glucose intolerance by FXR deficiency was previously reported and observed in our study, but the underlying biology remained unclear. To investigate the ambiguity, we collected the nontargeted profiles of the fecal metaproteome, serum metabolome, and liver proteome in Fxr-null (Fxr-/-) and wild-type (WT) mice with LC-HRMS. FXR deficiency showed a global impact on the different molecular levels we monitored, suggesting its serious disruption in the gut microbiota, hepatic metabolism, and circulating biomolecules. The network and enrichment analyses of the dysregulated metabolites and proteins suggested the perturbation of carbohydrate and lipid metabolism by FXR deficiency. Fxr-/- mice presented lower levels of hepatic proteins involved in glycogenesis. The impairment of glycogenesis by an FXR deficiency may leave glucose to accumulate in the circulation, which may deteriorate glucose tolerance. Lipid metabolism was dysregulated by FXR deficiency in a structural-dependent manner. Fatty acid β-oxidations were alleviated, but cholesterol metabolism was promoted by an FXR deficiency. Together, we explored the molecular events associated with glucose intolerance by impaired FXR with integrated novel multiomic data.

Dosing Strategies and Quantitative Clinical Pharmacology for Bispecific T-Cell Engagers Development in Oncology

Bispecific T-cell Engagers (TCEs) are promising anti-cancer treatments that bind to both the CD3 receptors on T cells and an antigen on the surface of tumor cells, creating an immune synapse, leading to killing of malignant tumor cells. These novel therapies have unique development challenges, with specific safety risks of cytokine release syndrome. These on-target adverse events fortunately can be mitigated and deconvoluted from efficacy via innovative dosing strategies, making clinical pharmacology key in the development of these therapies. This review assesses dose selection and the role of quantitative clinical pharmacology in the development of the first eight approved TCEs. Model informed drug development (MIDD) strategies can be used at every stage to guide TCE development. Mechanistic modeling approaches allow for (1) efficacious yet safe first-in-human dose selection as compared with in vitro minimum anticipated biological effect level (MABEL) approach; (2) rapid escalation and reducing number of patients with subtherapeutic doses through model-based adaptive design; (3) virtual testing of different step-up dosing regimens that may not be feasible to be evaluated in the clinic; and (4) selection and justification of the optimal clinical step-up and full treatment doses. As the knowledge base around TCEs continues to grow, the relevance and utilization of MIDD strategies for supporting the development and dose optimization of these molecules are expected to advance, optimizing the benefit-risk profile for cancer patients.

Patient-specific computational models predict prognosis in B cell lymphoma by quantifying pro-proliferative and anti-apoptotic signatures from genetic sequencing data

Genetic heterogeneity and co-occurring driver mutations impact clinical outcomes in blood cancers, but predicting the emergent effect of co-occurring mutations that impact multiple complex and interacting signalling networks is challenging. Here, we used mathematical models to predict the impact of co-occurring mutations on cellular signalling and cell fates in diffuse large B cell lymphoma and multiple myeloma. Simulations predicted adverse impact on clinical prognosis when combinations of mutations induced both anti-apoptotic (AA) and pro-proliferative (PP) signalling. We integrated patient-specific mutational profiles into personalised lymphoma models, and identified patients characterised by simultaneous upregulation of anti-apoptotic and pro-proliferative (AAPP) signalling in all genomic and cell-of-origin classifications (8-25% of patients). In a discovery cohort and two validation cohorts, patients with upregulation of neither, one (AA or PP), or both (AAPP) signalling states had good, intermediate and poor prognosis respectively. Combining AAPP signalling with genetic or clinical prognostic predictors reliably stratified patients into striking prognostic categories. AAPP patients in poor prognosis genetic clusters had 7.8 months median overall survival, while patients lacking both features had 90% overall survival at 120 months in a validation cohort. Personalised computational models enable identification of novel risk-stratified patient subgroups, providing a valuable tool for future risk-adapted clinical trials.

CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation

CRISPR gene editing strategies are shaping cell therapies through precise and tunable control over gene expression. However, achieving reliable therapeutic effects with improved safety and efficacy requires informed target gene selection. This depends on a thorough understanding of the involvement of target genes in gene regulatory networks (GRNs) that regulate cell phenotype and function. Machine learning models have been previously used for GRN reconstruction using RNA- seq data, but current techniques are limited to single cell types and focus mainly on transcription factors. This restriction overlooks many potential CRISPR target genes, such as those encoding extracellular matrix components, growth factors, and signaling molecules, thus limiting the applicability of these models for CRISPR strategies. To address these limitations, we have developed CRISPR-GEM, a multi-layer perceptron (MLP)-based synthetic GRN constructed to accurately predict the downstream effects of CRISPR gene editing. First, input and output nodes are identified as differentially expressed genes between defined experimental and target cell/tissue types respectively. Then, MLP training learns regulatory relationships in a black-box approach allowing accurate prediction of output gene expression using only input gene expression. Finally, CRISPR-mimetic perturbations are made to each input gene individually and the resulting model predictions are compared to those for the target group to score and assess each input gene as a CRISPR candidate. The top scoring genes provided by CRISPR-GEM therefore best modulate experimental group GRNs to motivate transcriptomic shifts towards a target group phenotype. This machine learning model is the first of its kind for predicting optimal CRISPR target genes and serves as a powerful tool for enhanced CRISPR strategies across a range of cell therapies.

Modeling reveals the strength of weak interactions in stacked-ring assembly

Cells employ many large macromolecular machines for the execution and regulation of processes that are vital for cell and organismal viability. Interestingly, cells cannot synthesize these machines as functioning units. Instead, cells synthesize the molecular parts that must then assemble into the functional complex. Many important machines, including chaperones such as GroEL and proteases such as the proteasome, comprise protein rings that are stacked on top of one another. While there is some experimental data regarding how stacked-ring complexes such as the proteasome self-assemble, a comprehensive understanding of the dynamics of stacked-ring assembly is currently lacking. Here, we developed a mathematical model of stacked-trimer assembly and performed an analysis of the assembly of the stacked homomeric trimer, which is the simplest stacked-ring architecture. We found that stacked rings are particularly susceptible to a form of kinetic trapping that we term "deadlock," in which the system gets stuck in a state where there are many large intermediates that are not the fully assembled structure but that cannot productively react. When interaction affinities are uniformly strong, deadlock severely limits assembly yield. We thus predicted that stacked rings would avoid situations where all interfaces in the structure have high affinity. Analysis of available crystal structures indicated that indeed the majority-if not all-of stacked trimers do not contain uniformly strong interactions. Finally, to better understand the origins of deadlock, we developed a formal pathway analysis and showed that, when all the binding affinities are strong, many of the possible pathways are utilized. In contrast, optimal assembly strategies utilize only a small number of pathways. Our work suggests that deadlock is a critical factor influencing the evolution of macromolecular machines and provides general principles for understanding the self-assembly efficiency of existing machines.

Less is more: Exploring neoadjuvant immunotherapy as a de-escalation strategy in head and neck squamous cell carcinoma treatment

Head and neck squamous cell carcinoma (HNSCC) constitutes a significant global cancer burden, given its high prevalence and associated mortality. Despite substantial progress in survival rates due to the enhanced multidisciplinary approach to treatment, these methods often lead to severe tissue damage, compromised function, and potential toxicity. Thus, there is an imperative need for novel, effective, and minimally damaging treatment modalities. Neoadjuvant treatment, an emerging therapeutic strategy, is designed to reduce tumor size and curtail distant metastasis prior to definitive intervention. Currently, neoadjuvant chemotherapy (NACT) has optimized the treatment approach for a subset of HNSCC patients, yet it has not produced a noticeable enhancement in overall survival (OS). In the contemporary cancer therapeutics landscape, immunotherapy is gaining traction at an accelerated pace. Notably, neoadjuvant immunotherapy (NAIT) has shown promising radiological and pathological responses, coupled with encouraging efficacy in several clinical trials. This potentially paves the way for a myriad of possibilities in treatment de-escalation of HNSCC, which warrants further exploration. This paper reviews the existing strategies and efficacies of neoadjuvant immune checkpoint inhibitors (ICIs), along with potential de-escalation strategies. Furthermore, the challenges encountered in the context of the de-escalation strategies of NAIT are explored. The aim is to inform future research directions that strive to improve the quality of life (QoL) for patients battling HNSCC.

Systems-level computational modeling in ischemic stroke: from cells to patients

Ischemic stroke, a significant threat to human life and health, refers to a class of conditions where brain tissue damage is induced following decreased cerebral blood flow. The incidence of ischemic stroke has been steadily increasing globally, and its disease mechanisms are highly complex and involve a multitude of biological mechanisms at various scales from genes all the way to the human body system that can affect the stroke onset, progression, treatment, and prognosis. To complement conventional experimental research methods, computational systems biology modeling can integrate and describe the pathogenic mechanisms of ischemic stroke across multiple biological scales and help identify emergent modulatory principles that drive disease progression and recovery. In addition, by running virtual experiments and trials in computers, these models can efficiently predict and evaluate outcomes of different treatment methods and thereby assist clinical decision-making. In this review, we summarize the current research and application of systems-level computational modeling in the field of ischemic stroke from the multiscale mechanism-based, physics-based and omics-based perspectives and discuss how modeling-driven research frameworks can deliver insights for future stroke research and drug development.

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.

AI-powered microscopy image analysis for parasitology: integrating human expertise

Microscopy image analysis plays a pivotal role in parasitology research. Deep learning (DL), a subset of artificial intelligence (AI), has garnered significant attention. However, traditional DL-based methods for general purposes are data-driven, often lacking explainability due to their black-box nature and sparse instructional resources. To address these challenges, this article presents a comprehensive review of recent advancements in knowledge-integrated DL models tailored for microscopy image analysis in parasitology. The massive amounts of human expert knowledge from parasitologists can enhance the accuracy and explainability of AI-driven decisions. It is expected that the adoption of knowledge-integrated DL models will open up a wide range of applications in the field of parasitology.

Application of machine learning approaches for predicting hemophilia A severity

BACKGROUND

Hemophilia A (HA) is an X-linked congenital bleeding disorder, which leads to deficiency of clotting factor (F) VIII. It mostly affects males, and females are considered carriers. However, it is now recognized that variants of F8 in females can result in HA. Nonetheless, most females go undiagnosed and untreated for HA, and their bleeding complications are attributed to other causes. Predicting the severity of HA for female patients can provide valuable insights for treating the conditions associated with the disease, such as heavy bleeding.

OBJECTIVES

To predict the severity of HA based on F8 genotype using a machine learning (ML) approach.

METHODS

Using multiple datasets of variants in the F8 and disease severity from various repositories, we derived the sequence for the FVIII protein. Using the derived sequences, we used ML models to predict the severity of HA in female patients.

RESULTS

Utilizing different classification models, we highlight the validity of the datasets and our approach with predictive F1 scores of 0.88, 0.99, 0.93, 0.99, and 0.90 for all the validation sets.

CONCLUSION

Although with some limitations, ML-based approaches demonstrated the successful prediction of disease severity in female HA patients based on variants in the F8. This study confirms previous research findings that ML can help predict the severity of hemophilia. These results can be valuable for future studies in achieving better treatment and clinical outcomes for female patients with HA, which is an urgent unmet need.

Dysregulated ribosome quality control in human diseases

Precise regulation of mRNA translation is of fundamental importance for maintaining homeostasis. Conversely, dysregulated general or transcript-specific translation, as well as abnormal translation events, have been linked to a multitude of diseases. However, driven by the misconception that the transient nature of mRNAs renders their abnormalities inconsequential, the importance of mechanisms that monitor the quality and fidelity of the translation process has been largely overlooked. In recent years, there has been a dramatic shift in this paradigm, evidenced by several seminal discoveries on the role of a key mechanism in monitoring the quality of mRNA translation - namely, Ribosome Quality Control (RQC) - in the maintenance of homeostasis and the prevention of diseases. Here, we will review recent advances in the field and emphasize the biological significance of the RQC mechanism, particularly its implications in human diseases.

Graph machine learning for integrated multi-omics analysis

Multi-omics experiments at bulk or single-cell resolution facilitate the discovery of hypothesis-generating biomarkers for predicting response to therapy, as well as aid in uncovering mechanistic insights into cellular and microenvironmental processes. Many methods for data integration have been developed for the identification of key elements that explain or predict disease risk or other biological outcomes. The heterogeneous graph representation of multi-omics data provides an advantage for discerning patterns suitable for predictive/exploratory analysis, thus permitting the modeling of complex relationships. Graph-based approaches-including graph neural networks-potentially offer a reliable methodological toolset that can provide a tangible alternative to scientists and clinicians that seek ideas and implementation strategies in the integrated analysis of their omics sets for biomedical research. Graph-based workflows continue to push the limits of the technological envelope, and this perspective provides a focused literature review of research articles in which graph machine learning is utilized for integrated multi-omics data analyses, with several examples that demonstrate the effectiveness of graph-based approaches.

Stability Analysis for Large-Scale Multi-Agent Molecular Communication Systems

Molecular communication (MC) is recently featured as a novel communication tool to connect individual biological nanorobots. It is expected that a large number of nanorobots can form large multi-agent MC systems through MC to accomplish complex and large-scale tasks that cannot be achieved by a single nanorobot. However, most previous models for MC systems assume a unidirectional diffusion communication channel and cannot capture the feedback between each nanorobot, which is important for multi-agent MC systems. In this paper, we introduce a system theoretic model for large-scale multi-agent MC systems using transfer functions, and then propose a method to analyze the stability for multi-agent MC systems. The proposed method decomposes the multi-agent MC system into multiple single-input and single-output (SISO) systems, which facilitates the application of simple analysis technique for SISO systems to the large-scale multi-agent MC system. Finally, we demonstrate the proposed method by analyzing the stability of a specific large-scale multi-agent MC system and clarify a parameter region to synchronize the states of nanorobots, which is important to make cooperative behaviors at a population level.

D'or: deep orienter of protein-protein interaction networks

MOTIVATION

Protein-protein interactions (PPIs) provide the skeleton for signal transduction in the cell. Current PPI measurement techniques do not provide information on their directionality which is critical for elucidating signaling pathways. To date, there are hundreds of thousands of known PPIs in public databases, yet only a small fraction of them have an assigned direction. This information gap calls for computational approaches for inferring the directionality of PPIs, aka network orientation.

RESULTS

In this work, we propose a novel deep learning approach for PPI network orientation. Our method first generates a set of proximity scores between a protein interaction and sets of cause and effect proteins using a network propagation procedure. Each of these score sets is fed, one at a time, to a deep set encoder whose outputs are used as features for predicting the interaction's orientation. On a comprehensive dataset of oriented PPIs taken from five different sources, we achieve an area under the precision-recall curve of 0.89-0.92, outperforming previous methods. We further demonstrate the utility of the oriented network in prioritizing cancer driver genes and disease genes.

AVAILABILITY AND IMPLEMENTATION

D'or is implemented in Python and is publicly available at https://github.com/pirakd/DeepOrienter.

Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making

The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.

Accelerated microbial identification "directly" from positive blood cultures using MALDI-TOF MS: Local clinical laboratory challenges

Rapid identification of microbial pathogens "directly" from positive blood cultures (PBCs) is critical for prompt initiation of empirical antibiotic therapy and clinical outcomes. Towards higher microbial identification rates, we modified a published initial serum separator tubes-based MALDI-TOF-MS protocol, for blood culture specimens received at a non-hospital based standalone diagnostic laboratory, Bangalore, India: (a) "Initial" protocol #1: From 28 PBCs, identification= 39% (Gram-negative= 43%: Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa; Gram-positive: 36%: Enterococcus faecalis, Staphylococcus aureus, Staphylococcus haemolyticus); mis-identification= 14%; non-identification= 47%. (b) "Modified" protocol #2: Quality controls (ATCC colonies spiked in negative blood cultures) From 7 analysis, identification= 100% (Escherichia coli, Klebsiella pneumonia, Klebsiella oxytoca, Pseudomonas aeruginosa, Enterococcus faecalis, Staphylococcus aureus); From 7 PBCs, identification= 57%; mis-identification= 14%; non-identification= 29%. Microbial preparations of highest quality and quantity for proteomic analysis and separate spectra matching reference databases for colonies and PBCs are needed for best clinical utility.

Mass spectrometry-based proteomic approaches for salivary protein biomarkers discovery and dental caries diagnosis: A critical review

Dental caries is a multifactorial chronic disease resulting from the intricate interplay among acid-generating bacteria, fermentable carbohydrates, and several host factors such as saliva. Saliva comprises several proteins which could be utilized as biomarkers for caries prevention, diagnosis, and prognosis. Mass spectrometry-based salivary proteomics approaches, owing to their sensitivity, provide the opportunity to investigate and unveil crucial cariogenic pathogen activity and host indicators and may demonstrate clinically relevant biomarkers to improve caries diagnosis and management. The present review outlines the published literature of human clinical proteomics investigations on caries and extensively elucidates frequently reported salivary proteins as biomarkers. This review also discusses important aspects while designing an experimental proteomics workflow. The protein-protein interactions and the clinical relevance of salivary proteins as biomarkers for caries, together with uninvestigated domains of the discipline are also discussed critically.

Incidence of hyperkalemia in anuric hemodialysis patients treated with sacubitril/valsartan

INTRODUCTION

Sacubitril/valsartan is increasingly used in hemodialysis patients due to its cardioprotective benefits. However, its impact on serum potassium levels in anuric patients undergoing hemodialysis remains controversial.

METHODS

We conducted a retrospective data from patients undergoing hemodialysis at two dialysis centers. A total of 71 out of 332 patients receiving hemodialysis treatment were enrolled. Mean serum potassium (mean value of 6-8 determinations), peak serum potassium (maximum K value observed during follow-up observations), and other biochemical parameters were recorded at baseline and during the follow-up period.

FINDINGS

After 6 months of follow-up, mean serum potassium increased from 4.84 ± 0.45 mmol/L at baseline to 5.07 ± 0.46 mmol/L at 3 months and 5.04 ± 0.46 mmol/L at 6 months (p < 0.001). Notably, no significant group differences were found in peak serum potassium concentrations between baseline and 6 months after sacubitril/valsartan therapy (5.69 ± 0.56 vs. 5.75 ± 0.41, p = 0.419). Prior to starting sacubitril/valsartan treatment, none of the patients had severe hyperkalemia; however, after 3 and 6 months of sacubitril/valsartan therapy, two (2.80%) and three (4.20%) patients experienced severe hyperkalemia, respectively; however, this difference was not statistically significant. Additionally, there was a significant reduction in blood pressure; however, serum sodium, bicarbonate, and Kt/V values did not change significantly during either period.

DISCUSSION

Sacubitril/valsartan therapy is associated with an increase in serum potassium levels in anuric hemodialysis patients. Nevertheless, the proportion of patients with severe hyperkalemia did not increase significantly. This suggests that the use of sacubitril/valsartan in anuric patients on hemodialysis is relatively safe.

Study on the citrus greening disease: Current challenges and novel therapies

The unprecedented worldwide spread of the Citrus greening disorder, called Huanglongbing (HLB), has urged researchers for rapid interventions. HLB poses a considerable threat to global citriculture owing to its devastating impact on citrus species. This disease is caused by Candidatus Liberibacter species (CLs), primarily transferred through psyllid insects, such as Trioza erytreae and Diaphorina citri. It results in phloem malfunction, root decline, and altered plant source-sink relationships, leading to a deficient plant with minimal yield before it dies. Thus, many various techniques have been employed to eliminate HLB and control vector populations through the application of insecticides and antimicrobials. The latter have evidenced short-term efficiency. While nucleic acid-based analyses and symptom-based identification of the disease have been used for detection, they suffer from limitations such as false negatives, complex sample preparation, and high costs. To address these challenges, secreted protein-based biomarkers offer a promising solution for accurate, rapid, and cost-effective disease detection. This paper presents an overview of HLB symptoms in citrus plants, including leaf and fruit symptoms, as well as whole tree symptoms. The differentiation between HLB symptoms and those of nutrient deficiencies is discussed, emphasizing the importance of precise identification for effective disease management. The elusive nature of CLs and the challenges in culturing them in axenic cultures have hindered the understanding of their pathogenic mechanisms. However, genome sequencing has provided insights into CLs strains' metabolic traits and potential virulence factors. Efforts to identify potential host target genes for resistance are discussed, and a high-throughput antimicrobial testing method using Citrus hairy roots is introduced as a promising tool for rapid assessment of potential treatments. This review summarizes current challenges and novel therapies for HLB disease. It highlights the urgency of developing accurate and efficient detection methods and identifying the complex relations between CLs and their host plants. Transgenic citrus in conjunction with secreted protein-based biomarkers and innovative testing methodologies could revolutionize HLB management strategies toward achieving a sustainable citrus cultivation. It offers more reliable and practical solutions to combat this devastating disease and safeguard the global citriculture industry.

Mathematical modeling of brain metastases growth and response to therapies: A review

Brain metastases (BMs) are the most common intracranial tumor type and a significant health concern, affecting approximately 10% to 30% of all oncological patients. Although significant progress is being made, many aspects of the metastatic process to the brain and the growth of the resulting lesions are still not well understood. There is a need for an improved understanding of the growth dynamics and the response to treatment of these tumors. Mathematical models have been proven valuable for drawing inferences and making predictions in different fields of cancer research, but few mathematical works have considered BMs. This comprehensive review aims to establish a unified platform and contribute to fostering emerging efforts dedicated to enhancing our mathematical understanding of this intricate and challenging disease. We focus on the progress made in the initial stages of mathematical modeling research regarding BMs and the significant insights gained from such studies. We also explore the vital role of mathematical modeling in predicting treatment outcomes and enhancing the quality of clinical decision-making for patients facing BMs.

Fusion-fission-mitophagy cycling and metabolic reprogramming coordinate nerve growth factor (NGF)-dependent neuronal differentiation

Neuronal differentiation is regulated by nerve growth factor (NGF) and other neurotrophins. We explored the impact of NGF on mitochondrial dynamics and metabolism through time-lapse imaging, metabolomics profiling, and computer modeling studies. We show that NGF may direct differentiation by stimulating fission, thereby causing selective mitochondrial network fragmentation and mitophagy, ultimately leading to increased mitochondrial quality and respiration. Then, we reconstructed the dynamic fusion-fission-mitophagy cycling of mitochondria in a computer model, integrating these processes into a single network mechanism. Both the computational model and the simulations are able to reproduce the proposed mechanism in terms of mitochondrial dynamics, levels of reactive oxygen species (ROS), mitophagy, and mitochondrial quality, thus providing a computational tool for the interpretation of the experimental data and for future studies aiming to detail further the action of NGF on mitochondrial processes. We also show that changes in these mitochondrial processes are intertwined with a metabolic function of NGF in differentiation: NGF directs a profound metabolic rearrangement involving glycolysis, TCA cycle, and the pentose phosphate pathway, altering the redox balance. This metabolic rewiring may ensure: (a) supply of both energy and building blocks for the anabolic processes needed for morphological reorganization, as well as (b) redox homeostasis.

Biophysical Modeling of Synaptic Plasticity

Dendritic spines are small, bulbous compartments that function as postsynaptic sites and undergo intense biochemical and biophysical activity. The role of the myriad signaling pathways that are implicated in synaptic plasticity is well studied. A recent abundance of quantitative experimental data has made the events associated with synaptic plasticity amenable to quantitative biophysical modeling. Spines are also fascinating biophysical computational units because spine geometry, signal transduction, and mechanics work in a complex feedback loop to tune synaptic plasticity. In this sense, ideas from modeling cell motility can inspire us to develop multiscale approaches for predictive modeling of synaptic plasticity. In this article, we review the key steps in postsynaptic plasticity with a specific focus on the impact of spine geometry on signaling, cytoskeleton rearrangement, and membrane mechanics. We summarize the main experimental observations and highlight how theory and computation can aid our understanding of these complex processes.

Inhibition of leukotriene-B4 signalling-mediated host response to tuberculosis is a potential mode of adjunctive host-directed therapy

Treatment of tuberculosis (TB) is faced with several challenges including the long treatment duration, drug toxicity and tissue pathology. Host-directed therapy provides promising avenues to find compounds for adjunctively assisting antimycobacterials in the TB treatment regimen, by promoting pathogen eradication or limiting tissue destruction. Eicosanoids are a class of lipid molecules that are potent mediators of inflammation and have been implicated in aspects of the host response against TB. Here, we have explored the blood transcriptome of pulmonary TB patients to understand the activity of leukotriene B4, a pro-inflammatory eicosanoid. Our study shows a significant upregulation in the leukotriene B4 signalling pathway in active TB patients, which is reversed with TB treatment. We have further utilized our in-house network analysis algorithm, ResponseNet, to identify potential downstream signal effectors of leukotriene B4 in TB patients including STAT1/2 and NADPH oxidase at a systemic as well as local level, followed by experimental validation of the same. Finally, we show the potential of inhibiting leukotriene B4 signalling as a mode of adjunctive host-directed therapy against TB. This study provides a new mode of TB treatment along with mechanistic insights which can be further explored in pre-clinical trials.

Self-organization underlies developmental robustness in plants

Development is a self-organized process that builds on cells and their interactions. Cells are heterogeneous in gene expression, growth, and division; yet how development is robust despite such heterogeneity is a fascinating question. Here, we review recent progress on this topic, highlighting how developmental robustness is achieved through self-organization. We will first discuss sources of heterogeneity, including stochastic gene expression, heterogeneity in growth rate and direction, and heterogeneity in division rate and precision. We then discuss cellular mechanisms that buffer against such noise, including Paf1C- and miRNA-mediated denoising, spatiotemporal growth averaging and compensation, mechanisms to improve cell division precision, and coordination of growth rate and developmental timing between different parts of an organ. We also discuss cases where such heterogeneity is not buffered but utilized for development. Finally, we highlight potential directions for future studies of noise and developmental robustness.

Discovering mechanisms of human genetic variation and controlling cell states at scale

Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.

Characterization of the plasma proteomic profile of Fabry disease: Potential sex- and clinical phenotype-specific biomarkers

Fabry disease (FD) is a X-linked rare lysosomal storage disorder caused by deficient α-galactosidase A (α-GalA) activity. Early diagnosis and the prediction of disease course are complicated by the clinical heterogeneity of FD, as well as by the frequently inconclusive biochemical and genetic test results that do not correlate with clinical course. We sought to identify potential biomarkers of FD to better understand the underlying pathophysiology and clinical phenotypes. We compared the plasma proteomes of 50 FD patients and 50 matched healthy controls using DDA and SWATH-MS. The >30 proteins that were differentially expressed between the 2 groups included proteins implicated in processes such as inflammation, heme and haemoglobin metabolism, oxidative stress, coagulation, complement cascade, glucose and lipid metabolism, and glycocalyx formation. Stratification by sex revealed that certain proteins were differentially expressed in a sex-dependent manner. Apolipoprotein A-IV was upregulated in FD patients with complications, especially those with chronic kidney disease, and apolipoprotein C-III and fetuin-A were identified as possible markers of FD with left ventricular hypertrophy. All these proteins had a greater capacity to identify the presence of complications in FD patients than lyso-GB3, with apolipoprotein A-IV standing out as being more sensitive and effective in differentiating the presence and absence of chronic kidney disease in FD patients than renal markers such as creatinine, glomerular filtration rate and microalbuminuria. Identification of these potential biomarkers can help further our understanding of the pathophysiological processes that underlie the heterogeneous clinical manifestations associated with FD.

Characterization and functional analysis of chicken CDK protein

The family of cell cycle-dependent kinases (CDKs) serves as catalytic subunits within protein kinase complexes, playing a crucial role in cell cycle progression. While the function of CDK proteins in regulating mammalian innate immune responses and virus replication is well-documented, their role in chickens remains unclear. To address this, we cloned several chicken CDKs, specifically CDK6 through CDK10. We observed that CDK6 is widely expressed across various chicken tissues, with localization in the cytoplasm, nucleus, or both in DF-1 cells. In addition, we also found that multiple chicken CDKs negatively regulate IFN-β signaling induced by chicken MAVS or chicken STING by targeting different steps. Moreover, during infection with infectious bursal disease virus (IBDV), various chicken CDKs, except CDK10, were recruited and co-localized with viral protein VP1. Interestingly, overexpression of CDK6 in chickens significantly enhanced IBDV replication. Conversely, knocking down CDK6 led to a marked increase in IFN-β production, triggered by chMDA5. Furthermore, targeting endogenous CDK6 with RNA interference substantially reduced IBDV replication. These findings collectively suggest that chicken CDKs, particularly CDK6, act as suppressors of IFN-β production and play a facilitative role in IBDV replication.

[Artificial intelligence in kidney transplant pathology]

BACKGROUND

Artificial intelligence (AI) systems have showed promising results in digital pathology, including digital nephropathology and specifically also kidney transplant pathology.

AIM

Summarize the current state of research and limitations in the field of AI in kidney transplant pathology diagnostics and provide a future outlook.

MATERIALS AND METHODS

Literature search in PubMed and Web of Science using the search terms "deep learning", "transplant", and "kidney". Based on these results and studies cited in the identified literature, a selection was made of studies that have a histopathological focus and use AI to improve kidney transplant diagnostics.

RESULTS AND CONCLUSION

Many studies have already made important contributions, particularly to the automation of the quantification of some histopathological lesions in nephropathology. This likely can be extended to automatically quantify all relevant lesions for a kidney transplant, such as Banff lesions. Important limitations and challenges exist in the collection of representative data sets and the updates of Banff classification, making large-scale studies challenging. The already positive study results make future AI support in kidney transplant pathology appear likely.

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights

Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.

Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics

BACKGROUND

Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention.

METHODS

We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets.

FINDINGS

We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions.

INTERPRETATION

Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

FUNDING

This work was supported by Cancer Research UK (grant no. C8221/A29017).

Positive GLI1/INHBA feedback loop drives tumor progression in gastric cancer

GLI1, a key transcription factor of the Hedgehog (Hh) signaling pathway, plays an important role in the development of cancer. However, the function and mechanisms by which GLI1 regulates gene transcription are not fully understood in gastric cancer (GC). Here, we found that GLI1 induced the proliferation and metastasis of GC cells, accompanied by transcriptional upregulation of INHBA. This increased INHBA expression exerted a promoting activity on Smads signaling and then transcriptionally activated GLI1 expression. Notably, our results demonstrate that disrupting the interaction between GLI1 and INHBA could inhibit GC tumorigenesis in vivo. More intriguingly, we confirmed the N6-methyladenosine (m6A) activation mechanism of the Helicobacter pylori/FTO/YTHDF2/GLI1 pathway in GC cells. In conclusion, our study confirmed that the GLI1/INHBA positive feedback loop influences GC progression and revealed the mechanism by which H. pylori upregulates GLI1 expression through m6A modification. This positive GLI1/INHBA feedback loop suggests a novel noncanonical mechanism of GLI1 activity in GC and provides potential therapeutic targets for GC treatment.

ScHGSC-IGDC: Identifying genes with differential correlations of high-grade serous ovarian cancer based on single-cell RNA sequencing analysis

Due to the high heterogeneity of ovarian cancer (OC), it occupies the main cause of cancer-related death among women. As the most aggressive and frequent subtype of OC, high-grade serous cancer (HGSC) represents around 70 % of all patients. With the booming progress of single-cell RNA sequencing (scRNA-seq), unique and subtle changes among different cell states have been identified including novel risk genes and pathways. Here, our present study aims to identify differentially correlated core genes between normal and tumor status through HGSC scRNA-seq data analysis. R package high-dimension Weighted Gene Co-expression Network Analysis (hdWGCNA) was implemented for building gene interaction networks based on HGSC scRNA-seq data. DiffCorr was integrated for identifying differentially correlated genes between tumor and their adjacent normal counterparts. Software Cytoscape was implemented for constructing and visualizing biological networks. Real-time qPCR (RT-qPCR) was utilized to confirm expression pattern of new genes. We introduced ScHGSC-IGDC (Identifying Genes with Differential Correlations of HGSC based on scRNA-seq analysis), an in silico framework for identifying core genes in the development of HGSC. We detected thirty-four modules in the network. Scores of new genes with opposite correlations with others such as NDUFS5, TMSB4X, SERPINE2 and ITPR2 were identified. Further survival and literature validation emphasized their great values in the HGSC management. Meanwhile, RT-qPCR verified expression pattern of NDUFS5, TMSB4X, SERPINE2 and ITPR2 in human OC cell lines and tissues. Our research offered novel perspectives on the gene modulatory mechanisms from single cell resolution, guiding network based algorithms in cancer etiology field.

P7C3 ameliorates barium chloride-induced skeletal muscle injury activating transcriptomic and epigenetic modulation of myogenic regulatory factors

Skeletal muscle injury affects the quality of life in many pathologies, including volumetric muscle loss, contusion injury, and aging. We hypothesized that the nicotinamide phosphoribosyltransferase (Nampt) activator P7C3 improves muscle repair following injury. In the present study, we tested the effect of P7C3 (1-anilino-3-(3,6-dibromocarbazol-9-yl) propan-2-ol) on chemically induced muscle injury. Muscle injury was induced by injecting 50 µL 1.2% barium chloride (BaCl2) into the tibialis anterior (TA) muscle in C57Bl/6J wild-type male mice. Mice were then treated with either 10 mg/kg body weight of P7C3 or Vehicle intraperitoneally for 7 days and assessed for histological, biochemical, and molecular changes. In the present study, we show that the acute BaCl2-induced TA muscle injury was robust and the P7C3-treated mice displayed a significant increase in the total number of myonuclei and blood vessels, and decreased serum CK activity compared with vehicle-treated mice. The specificity of P7C3 was evaluated using Nampt+/- mice, which did not display any significant difference in muscle repair capacity among treated groups. RNA-sequencing analysis of the injured TA muscles displayed 368 and 212 genes to be exclusively expressed in P7C3 and Veh-treated mice, respectively. There was an increase in the expression of genes involved in cellular processes, inflammatory response, angiogenesis, and muscle development in P7C3 versus Veh-treated mice. Conversely, there is a decrease in muscle structure and function, myeloid cell differentiation, glutathione, and oxidation-reduction, drug metabolism, and circadian rhythm signaling pathways. Chromatin immunoprecipitation-quantitative polymerase chain reaction (qPCR) and reverse transcription-qPCR analyses identified increased Pax7, Myf5, MyoD, and Myogenin expression in P7C3-treated mice. Increased histone lysine (H3K) methylation and acetylation were observed in P7C3-treated mice, with significant upregulation in inflammatory markers. Moreover, P7C3 treatment significantly increased the myotube fusion index in the BaCl2-injured human skeletal muscle in vitro. P7C3 also inhibited the lipopolysaccharide-induced inflammatory response and mitochondrial membrane potential of RAW 264.7 macrophage cells. Overall, we demonstrate that P7C3 activates muscle stem cells and enhances muscle injury repair with increased angiogenesis.

Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives

Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth, and the protein inhibits growth at high concentrations, establishing a positive feedback loop. For a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the commonly used single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.

GluK1 kainate receptors are necessary for functional maturation of parvalbumin interneurons regulating amygdala circuit function

Parvalbumin expressing interneurons (PV INs) are key players in the local inhibitory circuits and their developmental maturation coincides with the onset of adult-type network dynamics in the brain. Glutamatergic signaling regulates emergence of the unique PV IN phenotype, yet the receptor mechanisms involved are not fully understood. Here we show that GluK1 subunit containing kainate receptors (KARs) are necessary for development and maintenance of the neurochemical and functional properties of PV INs in the lateral and basal amygdala (BLA). Ablation of GluK1 expression specifically from PV INs resulted in low parvalbumin expression and loss of characteristic high firing rate throughout development. In addition, we observed reduced spontaneous excitatory synaptic activity at adult GluK1 lacking PV INs. Intriguingly, inactivation of GluK1 expression in adult PV INs was sufficient to abolish their high firing rate and to reduce PV expression levels, suggesting a role for GluK1 in dynamic regulation of PV IN maturation state. The PV IN dysfunction in the absence of GluK1 perturbed the balance between evoked excitatory vs. inhibitory synaptic inputs and long-term potentiation (LTP) in LA principal neurons, and resulted in aberrant development of the resting-state functional connectivity between mPFC and BLA. Behaviorally, the absence of GluK1 from PV INs associated with hyperactivity and increased fear of novelty. These results indicate a critical role for GluK1 KARs in regulation of PV IN function across development and suggest GluK1 as a potential therapeutic target for pathologies involving PV IN malfunction.

Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data

MOTIVATION

Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. The parameters of these models are commonly estimated from experimental data. Yet, experimental data generated from different techniques do not provide direct information about the state of the system but a nonlinear (monotonic) transformation of it. For such semi-quantitative data, when this transformation is unknown, it is not apparent how the model simulations and the experimental data can be compared.

RESULTS

We propose a versatile spline-based approach for the integration of a broad spectrum of semi-quantitative data into parameter estimation. We derive analytical formulas for the gradients of the hierarchical objective function and show that this substantially increases the estimation efficiency. Subsequently, we demonstrate that the method allows for the reliable discovery of unknown measurement transformations. Furthermore, we show that this approach can significantly improve the parameter inference based on semi-quantitative data in comparison to available methods.

AVAILABILITY AND IMPLEMENTATION

Modelers can easily apply our method by using our implementation in the open-source Python Parameter EStimation TOolbox (pyPESTO) available at https://github.com/ICB-DCM/pyPESTO.

Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets

Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors.

NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage

Objective. The safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters.Approach. A literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation.Main Results. We compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using akvalue of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%.Significance. This novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models.