MECP2 directly interacts with RNA polymerase II to modulate transcription in human neurons.

Mutations in the methyl-DNA-binding protein MECP2 cause the neurodevelopmental disorder Rett syndrome (RTT). How MECP2 contributes to transcriptional regulation in normal and disease states is unresolved; it has been reported to be an activator and a repressor. We describe here the first integrated CUT&Tag, transcriptome, and proteome analyses using human neurons with wild-type (WT) and mutant MECP2 molecules. MECP2 occupies CpG-rich promoter-proximal regions in over four thousand genes in human neurons, including a plethora of autism risk genes, together with RNA polymerase II (RNA Pol II). MECP2 directly interacts with RNA Pol II, and genes occupied by both proteins showed reduced expression in neurons with MECP2 patient mutations. We conclude that MECP2 acts as a positive cofactor for RNA Pol II gene expression at many neuronal genes that harbor CpG islands in promoter-proximal regions and that RTT is due, in part, to the loss of gene activity of these genes in neurons.

What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care.

Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electronic record by converting voice to text, and organized information from multiple sources within a health care system. AI/ML has less successfully recommended treatments for patients with complicated single diseases such as cancer; or improved diagnosing, patient shared decision making, and treating patients with multiple comorbidities and social determinant challenges. AI/ML has magnified disparities in health equity, and almost nothing is known of the effect of AI/ML on primary care physician-patient relationships. An intervention in Victoria, Australia showed promise where an AI/ML tool was used only as an adjunct to complex medical decision making. Putting these findings in a complex adaptive system framework, AI/ML tools will likely work when its tasks are limited in scope, have clean data that are mostly linear and deterministic, and fit well into existing workflows. AI/ML has rarely improved comprehensive care, especially in primary care settings, where data have a significant number of errors and inconsistencies. Primary care should be intimately involved in AI/ML development, and its tools carefully tested before implementation; and unlike electronic health records, not just assumed that AI/ML tools will improve primary care work life, quality, safety, and person-centered clinical decision making.

Patient Partnership Tools to Support Medication Safety in Community-Dwelling Older Adults: Protocol for a Nonrandomized Stepped Wedge Clinical Trial.

BACKGROUND: Preventable harms from medications are significant threats to patient safety in community settings, especially among ambulatory older adults on multiple prescription medications. Patients may partner with primary care professionals by taking on active roles in decisions, learning the basics of medication self-management, and working with community resources. OBJECTIVE: This study aims to assess the impact of a set of patient partnership tools that redesign primary care encounters to encourage and empower patients to make more effective use of those encounters to improve medication safety. METHODS: The study is a nonrandomized, cross-sectional stepped wedge cluster-controlled trial with 1 private family medicine clinic and 2 public safety-net primary care clinics each composing their own cluster. There are 2 intervention sequences with 1 cluster per sequence and 1 control sequence with 1 cluster. Cross-sectional surveys will be taken immediately at the conclusion of visits to the clinics during 6 time periods of 6 weeks each, with a transition period of no data collection during intervention implementation. The number of visits to be surveyed will vary by period and cluster. We plan to recruit patients and professionals for surveys during 405 visits. In the experimental periods, visits will be conducted with two partnership tools and associated clinic process changes: (1) a 1-page visit preparation guide given to relevant patients by clinic staff before seeing the provider, with the intention to improve communication and shared decision-making, and (2) a library of short educational videos that clinic staff encourage patients to watch on medication safety. In the control periods, visits will be conducted with usual care. The primary outcome will be patients' self-efficacy in medication use. The secondary outcomes are medication-related issues such as duplicate therapies identified by primary care providers and assessment of collaborative work during visits. RESULTS: The study was funded in September 2019. Data collection started in April 2023 and ended in December 2023. Data was collected for 405 primary care encounters during that period. As of February 15, 2024, initial descriptive statistics were calculated. Full data analysis is expected to be completed and published in the summer of 2024. CONCLUSIONS: This study will assess the impact of patient partnership tools and associated process changes in primary care on medication use self-efficacy and medication-related issues. The study is powered to identify types of patients who may benefit most from patient engagement tools in primary care visits. TRIAL REGISTRATION: ClinicalTrials.gov NCT05880368; https://clinicaltrials.gov/study/NCT05880368. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57878.

Chemical codes promote selective compartmentalization of proteins.

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must efficiently assemble. Such assembly is presumed to unfold as a result of specific interactions between biomolecules; however, recent evidence suggests that distinctive chemical environments within subcellular compartments may also play an important role. Here, we test the hypothesis that protein groups with shared functions also share codes that guide them to compartment destinations. To test our hypothesis, we developed a transformer large language model, called ProtGPS, that predicts with high performance the compartment localization of human proteins excluded from the training set. We then demonstrate ProtGPS can be used for guided generation of novel protein sequences that selectively assemble into specific compartments in cells. Furthermore, ProtGPS predictions were sensitive to disease-associated mutations that produce changes in protein compartmentalization, suggesting that this type of pathogenic dysfunction can be discovered in silico. Our results indicate that protein sequences contain not only a folding code, but also a previously unrecognized chemical code governing their distribution in specific cellular compartments.

A Distress-Processing Model for Clients in Suicidal Crisis.

Background: While crisis intervention frameworks have indicated the importance of clients in suicidal crisis better understanding their distress to decrease suicidality, it is unclear how clients in suicidal crisis process their distress. Aims: To develop (Study 1) and validate (Study 2) a sequential distress-processing model for clients in suicidal crisis. Methods: Applying task analysis, Study 1 consisted of three phases, which resulted in a theoretically and empirically informed model. In Study 2, we examined the distress-processing model's validity using a longitudinal design. In both studies, data were online crisis chats with adults in suicidal crisis. Results: In Study 1, we developed a sequential five-stage distress-processing model: (Stage 1) unengaged with distress, (Stage 2) distress awareness, (Stage 3) distress clarity, (Stage 4) distress insight, and (Stage 5) applying distress insight. In Study 2, the model's validity was supported via evidence that (H1) progression through the processing stages was sequential and (H2) clients with good outcomes had greater progression in their processing than clients with poor outcomes. Limitation: Clients who were suicidal but did not disclose their suicidality were not included. Conclusion: Our findings provide a framework for conceptualizing and operationalizing how clients move through suicidal crises, which can facilitate intervention and research developments.

Distinct chemical environments in biomolecular condensates.

Diverse mechanisms have been described for selective enrichment of biomolecules in membrane-bound organelles, but less is known about mechanisms by which molecules are selectively incorporated into biomolecular assemblies such as condensates that lack surrounding membranes. The chemical environments within condensates may differ from those outside these bodies, and if these differed among various types of condensate, then the different solvation environments would provide a mechanism for selective distribution among these intracellular bodies. Here we use small molecule probes to show that different condensates have distinct chemical solvating properties and that selective partitioning of probes in condensates can be predicted with deep learning approaches. Our results demonstrate that different condensates harbor distinct chemical environments that influence the distribution of molecules, show that clues to condensate chemical grammar can be ascertained by machine learning and suggest approaches to facilitate development of small molecule therapeutics with optimal subcellular distribution and therapeutic benefit.

Resident Involvement in Curricular and Clinical Practice Change and Satisfaction With Training According to Length of Training in Family Medicine.

BACKGROUND AND OBJECTIVES: Most research in residency training has focused on quality improvement within a single program. We explored resident involvement in curricular and clinical practice change, the learning environment, and resident satisfaction in 3-year family medicine residencies compared to matched 4-year residencies. METHODS: We used two surveys to capture data. One was for program directors, which assessed the level of resident involvement in curricular and practice transformation. The second was a resident survey, which asked residents to rate their involvement in curricular change and practice transformation, the learning environment, and satisfaction with training. Both were administered annually between 2013 and 2019. Response rates ranged from 84.6% to 100%. RESULTS: Findings revealed no overall difference in resident involvement in curricular change, but the program director survey findings indicated that a higher proportion of residents in 4-year programs were using a broader diversity of approaches to working on quality improvement (QI) projects compared to those in 3-year programs. We also found statistical differences in the number of QI projects completed per year, with 34.1% completing three or more in 4-year programs compared to 13.3% in 3-year programs (P<.001). We found a positive correlation between resident involvement, learning environment, and satisfaction with training for both 3-year (range 0.489-0.666; P=.001) and 4-year residents (range 0.441-0.529; P=.001). CONCLUSIONS: Four-year residents were involved in a greater number of quality improvement projects and had a more diverse profile of involvement than those in 3-year residency programs. Involvement in practice and curricular change and the learning environment were associated with greater levels of resident satisfaction with training in both 3-year and 4-year programs.

Primary care teams' reported actions to improve medication safety: a qualitative study with insights in high reliability organising.

BACKGROUND: Our aim was to understand actions by primary care teams to improve medication safety. METHODS: This was a qualitative study using one-on-one, semistructured interviews with the questions guided by concepts from collaborative care and systems engineering models, and with references to the care of older adults. We interviewed 21 primary care physicians and their team members at four primary care sites serving patients with mostly low socioeconomic status in Southwest US during 2019-2020. We used thematic analysis with a combination of inductive and deductive coding. First, codes capturing safety actions were incrementally developed and revised iteratively by a team of multidisciplinary analysts using the inductive approach. Themes that emerged from the coded safety actions taken by primary care professionals to improve medication safety were then mapped to key principles from the high reliability organisation framework using a deductive approach. RESULTS: Primary care teams described their actions in medication safety mainly in making standard-of-care medical decisions, patient-shared decision-making, educating patients and their caregivers, providing asynchronous care separate from office visits and providing clinical infrastructure. Most of the actions required customisation at the individual level, such as limiting the supply of certain medications prescribed and simplifying medication regimens in certain patients. Primary care teams enacted high reliability organisation principles by anticipating and mitigating risks and taking actions to build resilience in patient work systems. The primary care teams' actions reflected their safety organising efforts as responses to many other agents in multiple settings that they could not control nor easily coordinate. CONCLUSIONS: Primary care teams take many actions to shape medication safety outcomes in community settings, and these actions demonstrated that primary care teams are a reservoir of resilience for medication safety in the overall healthcare system. To improve medication safety, primary care work systems require different strategies than those often used in more self-contained systems such as hospital inpatient or surgical services.

Transcription factors interact with RNA to regulate genes.

Transcription factors (TFs) orchestrate the gene expression programs that define each cell's identity. The canonical TF accomplishes this with two domains, one that binds specific DNA sequences and the other that binds protein coactivators or corepressors. We find that at least half of TFs also bind RNA, doing so through a previously unrecognized domain with sequence and functional features analogous to the arginine-rich motif of the HIV transcriptional activator Tat. RNA binding contributes to TF function by promoting the dynamic association between DNA, RNA, and TF on chromatin. TF-RNA interactions are a conserved feature important for vertebrate development and disrupted in disease. We propose that the ability to bind DNA, RNA, and protein is a general property of many TFs and is fundamental to their gene regulatory function.

Genetic predisposition to neuroblastoma results from a regulatory polymorphism that promotes the adrenergic cell state.

Childhood neuroblastomas exhibit plasticity between an undifferentiated neural crest-like mesenchymal cell state and a more differentiated sympathetic adrenergic cell state. These cell states are governed by autoregulatory transcriptional loops called core regulatory circuitries (CRCs), which drive the early development of sympathetic neuronal progenitors from migratory neural crest cells during embryogenesis. The adrenergic cell identity of neuroblastoma requires LMO1 as a transcriptional cofactor. Both LMO1 expression levels and the risk of developing neuroblastoma in children are associated with a single nucleotide polymorphism, G/T, that affects a GATA motif in the first intron of LMO1. Here, we showed that WT zebrafish with the GATA genotype developed adrenergic neuroblastoma, while knock-in of the protective TATA allele at this locus reduced the penetrance of MYCN-driven tumors, which were restricted to the mesenchymal cell state. Whole genome sequencing of childhood neuroblastomas demonstrated that TATA/TATA tumors also exhibited a mesenchymal cell state and were low risk at diagnosis. Thus, conversion of the regulatory GATA to a TATA allele in the first intron of LMO1 reduced the neuroblastoma-initiation rate by preventing formation of the adrenergic cell state. This mechanism was conserved over 400 million years of evolution, separating zebrafish and humans.

Regulatory architecture of housekeeping genes is driven by promoter assemblies.

Genes that are key to cell identity are generally regulated by cell-type-specific enhancer elements bound by transcription factors, some of which facilitate looping to distant gene promoters. In contrast, genes that encode housekeeping functions, whose regulation is essential for normal cell metabolism and growth, generally lack interactions with distal enhancers. We find that Ronin (Thap11) assembles multiple promoters of housekeeping and metabolic genes to regulate gene expression. This behavior is analogous to how enhancers are brought together with promoters to regulate cell identity genes. Thus, Ronin-dependent promoter assemblies provide a mechanism to explain why housekeeping genes can forgo distal enhancer elements and why Ronin is important for cellular metabolism and growth control. We propose that clustering of regulatory elements is a mechanism common to cell identity and housekeeping genes but is accomplished by different factors binding distinct control elements to establish enhancer-promoter or promoter-promoter interactions, respectively.

Oncogenic CDK13 mutations impede nuclear RNA surveillance.

RNA surveillance pathways detect and degrade defective transcripts to ensure RNA fidelity. We found that disrupted nuclear RNA surveillance is oncogenic. Cyclin-dependent kinase 13 (CDK13) is mutated in melanoma, and patient-mutated CDK13 accelerates zebrafish melanoma. CDK13 mutation causes aberrant RNA stabilization. CDK13 is required for ZC3H14 phosphorylation, which is necessary and sufficient to promote nuclear RNA degradation. Mutant CDK13 fails to activate nuclear RNA surveillance, causing aberrant protein-coding transcripts to be stabilized and translated. Forced aberrant RNA expression accelerates melanoma in zebrafish. We found recurrent mutations in genes encoding nuclear RNA surveillance components in many malignancies, establishing nuclear RNA surveillance as a tumor-suppressive pathway. Activating nuclear RNA surveillance is crucial to avoid accumulation of aberrant RNAs and their ensuing consequences in development and disease.

CDK7 controls E2F- and MYC-driven proliferative and metabolic vulnerabilities in multiple myeloma.

Therapeutic targeting of CDK7 has proven beneficial in preclinical studies, yet the off-target effects of currently available CDK7 inhibitors make it difficult to pinpoint the exact mechanisms behind MM cell death mediated by CDK7 inhibition. Here, we show that CDK7 expression positively correlates with E2F and MYC transcriptional programs in cells from patients with multiple myeloma (MM); its selective targeting counteracts E2F activity via perturbation of the cyclin-dependent kinases/Rb axis and impairs MYC-regulated metabolic gene signatures translating into defects in glycolysis and reduced levels of lactate production in MM cells. CDK7 inhibition using the covalent small-molecule inhibitor YKL-5-124 elicits a strong therapeutic response with minimal effects on normal cells, and causes in vivo tumor regression, increasing survival in several mouse models of MM including a genetically engineered mouse model of MYC-dependent MM. Through its role as a critical cofactor and regulator of MYC and E2F activity, CDK7 is therefore a master regulator of oncogenic cellular programs supporting MM growth and survival, and a valuable therapeutic target providing rationale for development of YKL-5-124 for clinical use.

Genetic Predisposition to Neuroblastoma Results from a Regulatory Polymorphism that Promotes the Adrenergic Cell State.

Childhood neuroblastomas exhibit plasticity between an undifferentiated neural crest-like "mesenchymal" cell state and a more differentiated sympathetic "adrenergic" cell state. These cell states are governed by autoregulatory transcriptional loops called core regulatory circuitries (CRCs), which drive the early development of sympathetic neuronal progenitors from migratory neural crest cells during embryogenesis. The adrenergic cell identity of neuroblastoma requires LMO1 as a transcriptional co-factor. Both LMO1 expression levels and the risk of developing neuroblastoma in children are associated with a single nucleotide polymorphism G/T that affects a G ATA motif in the first intron of LMO1. Here we show that wild-type zebrafish with the G ATA genotype develop adrenergic neuroblastoma, while knock-in of the protective T ATA allele at this locus reduces the penetrance of MYCN-driven tumors, which are restricted to the mesenchymal cell state. Whole genome sequencing of childhood neuroblastomas demonstrates that T ATA/ T ATA tumors also exhibit a mesenchymal cell state and are low risk at diagnosis. Thus, conversion of the regulatory G ATA to a T ATA allele in the first intron of LMO1 reduces the neuroblastoma initiation rate by preventing formation of the adrenergic cell state, a mechanism that is conserved over 400 million years of evolution separating zebrafish and humans.

Bat pluripotent stem cells reveal unusual entanglement between host and viruses.

Bats are distinctive among mammals due to their ability to fly, use laryngeal echolocation, and tolerate viruses. However, there are currently no reliable cellular models for studying bat biology or their response to viral infections. Here, we created induced pluripotent stem cells (iPSCs) from two species of bats: the wild greater horseshoe bat (Rhinolophus ferrumequinum) and the greater mouse-eared bat (Myotis myotis). The iPSCs from both bat species showed similar characteristics and had a gene expression profile resembling that of cells attacked by viruses. They also had a high number of endogenous viral sequences, particularly retroviruses. These results suggest that bats have evolved mechanisms to tolerate a large load of viral sequences and may have a more intertwined relationship with viruses than previously thought. Further study of bat iPSCs and their differentiated progeny will provide insights into bat biology, virus host relationships, and the molecular basis of bats' special traits.

Action-project method: An approach to describing and studying goal-oriented joint actions.

CONTEXT: Practicing health professionals and educators frequently act together in an interdependent or joint capacity to reach goals. Teaching or learning a new skill or engaging with patients in shared decision-making exemplifies this joint and goal-directed nature of Health Professions Education (HPE) and practice. However, building a robust understanding of the complexity of action, and joint action in particular, in HPE or patient care remains a challenge because of a limited number of methodologies available within HPE research. METHODS: In this manuscript, we describe the Action-Project Method (A-PM) as a qualitative research approach that can be used to describe and understand goal-directed joint actions. A-PM is grounded in contextual action theory and is a methodology focussed on action as an object of study, as it is occurring. A-PM uses three distinct perspectives to understand goal-directed joint actions: observable behaviour, internal processes (i.e. reported thoughts and feelings) and the social meaning reflected in goals. Data collection in A-PM involves observations, interviews, recording of actions and a self-confrontation procedure-where participants watch video-recorded segments of action and reflect on their internal processes, describing what they were thinking or feeling as they were completing the action. Together, the rich data generated and the layered approach to analysis provide a means to better understand the joint actions embedded in complex systems and collaborative work. Furthermore, the participants are treated as equal partners within A-PM, ensuring data equity even when the research context includes hierarchical relationships. DISCUSSION: Given increasing recognition to the importance of teamwork, relationships, interdependence, complex environments and centring patient or learner voices, A-PM is a valuable research approach for HPE. A-PM deepens our research arsenal with an approach that focusses on interdependent dyads or teams and provides a deeper understanding for how individuals engage together in goal-oriented actions.

A MIR17HG-derived long noncoding RNA provides an essential chromatin scaffold for protein interaction and myeloma growth.

Long noncoding RNAs (lncRNAs) can drive tumorigenesis and are susceptible to therapeutic intervention. Here, we used a large-scale CRISPR interference viability screen to interrogate cell-growth dependency to lncRNA genes in multiple myeloma (MM) and identified a prominent role for the miR-17-92 cluster host gene (MIR17HG). We show that an MIR17HG-derived lncRNA, named lnc-17-92, is the main mediator of cell-growth dependency acting in a microRNA- and DROSHA-independent manner. Lnc-17-92 provides a chromatin scaffold for the functional interaction between c-MYC and WDR82, thus promoting the expression of ACACA, which encodes the rate-limiting enzyme of de novo lipogenesis acetyl-coA carboxylase 1. Targeting MIR17HG pre-RNA with clinically applicable antisense molecules disrupts the transcriptional and functional activities of lnc-17-92, causing potent antitumor effects both in vitro and in vivo in 3 preclinical animal models, including a clinically relevant patient-derived xenograft NSG mouse model. This study establishes a novel oncogenic function of MIR17HG and provides potent inhibitors for translation to clinical trials.

The dynamic clustering of insulin receptor underlies its signaling and is disrupted in insulin resistance.

Insulin receptor (IR) signaling is central to normal metabolic control and is dysregulated in metabolic diseases such as type 2 diabetes. We report here that IR is incorporated into dynamic clusters at the plasma membrane, in the cytoplasm and in the nucleus of human hepatocytes and adipocytes. Insulin stimulation promotes further incorporation of IR into these dynamic clusters in insulin-sensitive cells but not in insulin-resistant cells, where both IR accumulation and dynamic behavior are reduced. Treatment of insulin-resistant cells with metformin, a first-line drug used to treat type 2 diabetes, can rescue IR accumulation and the dynamic behavior of these clusters. This rescue is associated with metformin's role in reducing reactive oxygen species that interfere with normal dynamics. These results indicate that changes in the physico-mechanical features of IR clusters contribute to insulin resistance and have implications for improved therapeutic approaches.