Structurally Resolved SARS-CoV-2 Antibody Shows High Efficacy in Severely Infected Hamsters and Provides a Potent Cocktail Pairing Strategy.

Understanding how potent neutralizing antibodies (NAbs) inhibit SARS-CoV-2 is critical for effective therapeutic development. We previously described BD-368-2, a SARS-CoV-2 NAb with high potency; however, its neutralization mechanism is largely unknown. Here, we report the 3.5-Å cryo-EM structure of BD-368-2/trimeric-spike complex, revealing that BD-368-2 fully blocks ACE2 recognition by occupying all three receptor-binding domains (RBDs) simultaneously, regardless of their "up" or "down" conformations. Also, BD-368-2 treats infected adult hamsters at low dosages and at various administering windows, in contrast to placebo hamsters that manifested severe interstitial pneumonia. Moreover, BD-368-2's epitope completely avoids the common binding site of VH3-53/VH3-66 recurrent NAbs, evidenced by tripartite co-crystal structures with RBDs. Pairing BD-368-2 with a potent recurrent NAb neutralizes SARS-CoV-2 pseudovirus at pM level and rescues mutation-induced neutralization escapes. Together, our results rationalized a new RBD epitope that leads to high neutralization potency and demonstrated BD-368-2's therapeutic potential in treating COVID-19.

Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle.

Dissecting the regulatory mechanisms controlling mammalian transcripts from production to degradation requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and cytoplasm in human and mouse cells. These rates varied substantially, yet transcripts from genes with related functions or targeted by the same transcription factors and RNA-binding proteins flowed across subcellular compartments with similar kinetics. Verifying these associations uncovered a link between DDX3X and nuclear export. For hundreds of RNA metabolism genes, most transcripts with retained introns were degraded by the nuclear exosome, while the remaining molecules were exported with stable cytoplasmic lifespans. Transcripts residing on chromatin for longer had extended poly(A) tails, whereas the reverse was observed for cytoplasmic mRNAs. Finally, machine learning identified molecular features that predicted the diverse life cycles of mRNAs.

Distinct lineage-dependent structural and functional organization of the hippocampus.

The hippocampus, as part of the cerebral cortex, is essential for memory formation and spatial navigation. Although it has been extensively studied, especially as a model system for neurophysiology, the cellular processes involved in constructing and organizing the hippocampus remain largely unclear. Here, we show that clonally related excitatory neurons in the developing hippocampus are progressively organized into discrete horizontal, but not vertical, clusters in the stratum pyramidale, as revealed by both cell-type-specific retroviral labeling and mosaic analysis with double markers (MADM). Moreover, distinct from those in the neocortex, sister excitatory neurons in the cornu ammonis 1 region of the hippocampus rarely develop electrical or chemical synapses with each other. Instead, they preferentially receive common synaptic input from nearby fast-spiking (FS), but not non-FS, interneurons and exhibit synchronous synaptic activity. These results suggest that shared inhibitory input may specify horizontally clustered sister excitatory neurons as functional units in the hippocampus.

Viral afterlife: SARS-CoV-2 as a reservoir of immunomimetic peptides that reassemble into proinflammatory supramolecular complexes.

It is unclear how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection leads to the strong but ineffective inflammatory response that characterizes severe Coronavirus disease 2019 (COVID-19), with amplified immune activation in diverse cell types, including cells without angiotensin-converting enzyme 2 receptors necessary for infection. Proteolytic degradation of SARS-CoV-2 virions is a milestone in host viral clearance, but the impact of remnant viral peptide fragments from high viral loads is not known. Here, we examine the inflammatory capacity of fragmented viral components from the perspective of supramolecular self-organization in the infected host environment. Interestingly, a machine learning analysis to SARS-CoV-2 proteome reveals sequence motifs that mimic host antimicrobial peptides (xenoAMPs), especially highly cationic human cathelicidin LL-37 capable of augmenting inflammation. Such xenoAMPs are strongly enriched in SARS-CoV-2 relative to low-pathogenicity coronaviruses. Moreover, xenoAMPs from SARS-CoV-2 but not low-pathogenicity homologs assemble double-stranded RNA (dsRNA) into nanocrystalline complexes with lattice constants commensurate with the steric size of Toll-like receptor (TLR)-3 and therefore capable of multivalent binding. Such complexes amplify cytokine secretion in diverse uninfected cell types in culture (epithelial cells, endothelial cells, keratinocytes, monocytes, and macrophages), similar to cathelicidin's role in rheumatoid arthritis and lupus. The induced transcriptome matches well with the global gene expression pattern in COVID-19, despite using <0.3% of the viral proteome. Delivery of these complexes to uninfected mice boosts plasma interleukin-6 and CXCL1 levels as observed in COVID-19 patients.

The evening complex promotes maize flowering and adaptation to temperate regions.

Maize (Zea mays) originated in southern Mexico and has spread over a wide latitudinal range. Maize expansion from tropical to temperate regions has necessitated a reduction of its photoperiod sensitivity. In this study, we cloned a quantitative trait locus (QTL) regulating flowering time in maize and show that the maize ortholog of Arabidopsis thaliana EARLY FLOWERING3, ZmELF3.1, is the causal locus. We demonstrate that ZmELF3.1 and ZmELF3.2 proteins can physically interact with ZmELF4.1/4.2 and ZmLUX1/2, to form evening complex(es; ECs) in the maize circadian clock. Loss-of-function mutants for ZmELF3.1/3.2 and ZmLUX1/2 exhibited delayed flowering under long-day and short-day conditions. We show that EC directly represses the expression of several flowering suppressor genes, such as the CONSTANS, CONSTANS-LIKE, TOC1 (CCT) genes ZmCCT9 and ZmCCT10, ZmCONSTANS-LIKE 3, and the PSEUDORESPONSE REGULATOR (PRR) genes ZmPRR37a and ZmPRR73, thus alleviating their inhibition, allowing florigen gene expression and promoting flowering. Further, we identify two closely linked retrotransposons located in the ZmELF3.1 promoter that regulate the expression levels of ZmELF3.1 and may have been positively selected during postdomestication spread of maize from tropical to temperate regions during the pre-Columbian era. These findings provide insights into circadian clock-mediated regulation of photoperiodic flowering in maize and new targets of genetic improvement for breeding.

IDPpub: Illuminating the Dark Phosphoproteome Through PubMed Mining.

Global phosphoproteomics experiments quantify tens of thousands of phosphorylation sites. However, data interpretation is hampered by our limited knowledge on functions, biological contexts, or precipitating enzymes of the phosphosites. This study establishes a repository of phosphosites with associated evidence in biomedical abstracts, using deep learning-based natural language processing techniques. Our model for illuminating the dark phosphoproteome through PubMed mining (IDPpub) was generated by fine-tuning BioBERT, a deep learning tool for biomedical text mining. Trained using sentences containing protein substrates and phosphorylation site positions from 3000 abstracts, the IDPpub model was then used to extract phosphorylation sites from all MEDLINE abstracts. The extracted proteins were normalized to gene symbols using the National Center for Biotechnology Information gene query, and sites were mapped to human UniProt sequences using ProtMapper and mouse UniProt sequences by direct match. Precision and recall were calculated using 150 curated abstracts, and utility was assessed by analyzing the CPTAC (Clinical Proteomics Tumor Analysis Consortium) pan-cancer phosphoproteomics datasets and the PhosphoSitePlus database. Using 10-fold cross validation, pairs of correct substrates and phosphosite positions were extracted with an average precision of 0.93 and recall of 0.94. After entity normalization and site mapping to human reference sequences, an independent validation achieved a precision of 0.91 and recall of 0.77. The IDPpub repository contains 18,458 unique human phosphorylation sites with evidence sentences from 58,227 abstracts and 5918 mouse sites in 14,610 abstracts. This included evidence sentences for 1803 sites identified in CPTAC studies that are not covered by manually curated functional information in PhosphoSitePlus. Evaluation results demonstrate the potential of IDPpub as an effective biomedical text mining tool for collecting phosphosites. Moreover, the repository (http://idppub.ptmax.org), which can be automatically updated, can serve as a powerful complement to existing resources.

Integration of C3H15-mediated transcriptional and post-transcriptional regulation confers plant thermotolerance in Arabidopsis.

Heat stress is an environmental factor that significantly threatens crop production worldwide. Nevertheless, the molecular mechanisms governing plant responses to heat stress are not fully understood. Plant zinc finger CCCH proteins have roles in stress responses as well as growth and development through protein-RNA, protein-DNA, and protein-protein interactions. Here, we reveal an integrated multi-level regulation of plant thermotolerance that is mediated by the CCCH protein C3H15 in Arabidopsis. Heat stress rapidly suppressed C3H15 transcription, which attenuated C3H15-inhibited expression of its target gene HEAT SHOCK TRANSCRIPTION FACTOR A2 (HSFA2), a central regulator of heat stress response (HSR), thereby activating HEAT SHOCK COGNATE 70 (HSC70.3) expression. The RING-type E3 ligase MED25-BINDING RING-H2 PROTEIN 2 (MBR2) was identified as an interacting partner of C3H15. The mbr2 mutant was susceptible to heat stress compared to wild-type plants, whereas plants overexpressing MBR2 showed increased heat tolerance. MBR2-dependent ubiquitination mediated the degradation of phosphorylated C3H15 protein in the cytoplasm, which was enhanced by heat stress. Consistently, heat sensitivities of C3H15 overexpression lines increased in MBR2 loss-of-function and decreased in MBR2 overexpression backgrounds. Heat stress-induced accumulation of HSC70.3 promoted MBR2-mediated degradation of C3H15 protein, implying that an auto-regulatory loop involving C3H15, HSFA2, and HSC70.3 regulates HSR. Heat stress also led to the accumulation of C3H15 in stress granules (SGs), a kind of cytoplasmic RNA granule. This study advances our understanding of the mechanisms plants use to respond to heat stress, which will facilitate technologies to improve thermotolerance in crops.

Advancing entity recognition in biomedicine via instruction tuning of large language models.

MOTIVATION: Large Language Models (LLMs) have the potential to revolutionize the field of Natural Language Processing, excelling not only in text generation and reasoning tasks but also in their ability for zero/few-shot learning, swiftly adapting to new tasks with minimal fine-tuning. LLMs have also demonstrated great promise in biomedical and healthcare applications. However, when it comes to Named Entity Recognition (NER), particularly within the biomedical domain, LLMs fall short of the effectiveness exhibited by fine-tuned domain-specific models. One key reason is that NER is typically conceptualized as a sequence labeling task, whereas LLMs are optimized for text generation and reasoning tasks. RESULTS: We developed an instruction-based learning paradigm that transforms biomedical NER from a sequence labeling task into a generation task. This paradigm is end-to-end and streamlines the training and evaluation process by automatically repurposing pre-existing biomedical NER datasets. We further developed BioNER-LLaMA using the proposed paradigm with LLaMA-7B as the foundational LLM. We conducted extensive testing on BioNER-LLaMA across three widely recognized biomedical NER datasets, consisting of entities related to diseases, chemicals, and genes. The results revealed that BioNER-LLaMA consistently achieved higher F1-scores ranging from 5% to 30% compared to the few-shot learning capabilities of GPT-4 on datasets with different biomedical entities. We show that a general-domain LLM can match the performance of rigorously fine-tuned PubMedBERT models and PMC-LLaMA, biomedical-specific language model. Our findings underscore the potential of our proposed paradigm in developing general-domain LLMs that can rival SOTA performances in multi-task, multi-domain scenarios in biomedical and health applications. AVAILABILITY AND IMPLEMENTATION: Datasets and other resources are available at https://github.com/BIDS-Xu-Lab/BioNER-LLaMA.

Ubiquitinated DA1 negatively regulates vascular cambium activity through modulating the stability of WOX4 in Populus.

Activity of the vascular cambium gives rise to secondary xylem for wood formation in trees. The transcription factor WUSCHEL-related HOMEOBOX4 (WOX4) is a central regulator downstream of the hormone and peptide signaling pathways that maintain cambial activity. However, the genetic regulatory network underlying WOX4-mediated wood formation at the post-transcriptional level remains to be elucidated. In this study, we identified the ubiquitin receptor PagDA1 in hybrid poplar (Populus alba × Populus glandulosa clone 84K) as a negative regulator of wood formation, which restricts cambial activity during secondary growth. Overexpression of PagDA1 in poplar resulted in a relatively reduced xylem due to decreased cambial cell division. By contrast, mutation of PagDA1 by CRISPR/Cas9 resulted in an increased cambial cell activity and promoted xylem formation. Genetic analysis demonstrated that PagDA1 functions antagonistically in a common pathway as PagWOX4 to regulate cambial activity. We propose that PagDA1 physically associates with PagWOX4 and modulates the degradation of PagWOX4 by the 26S proteasome. Moreover, genetic analysis revealed that PagDA1 exerts its negative effect on cambial development by modulating the stability of PagWOX4 in a ubiquitin-dependent manner mediated by the E3 ubiquitin ligase PagDA2. In sum, we have identified a cambial regulatory protein complex, PagDA1-PagWOX4, as a potential target for wood biomass improvement.

Co-evolution-based prediction of metal-binding sites in proteomes by machine learning.

Metal ions have various important biological roles in proteins, including structural maintenance, molecular recognition and catalysis. Previous methods of predicting metal-binding sites in proteomes were based on either sequence or structural motifs. Here we developed a co-evolution-based pipeline named 'MetalNet' to systematically predict metal-binding sites in proteomes. We applied MetalNet to proteomes of four representative prokaryotic species and predicted 4,849 potential metalloproteins, which substantially expands the currently annotated metalloproteomes. We biochemically and structurally validated previously unannotated metal-binding sites in several proteins, including apo-citrate lyase phosphoribosyl-dephospho-CoA transferase citX, an Escherichia coli enzyme lacking structural or sequence homology to any known metalloprotein (Protein Data Bank (PDB) codes: 7DCM and 7DCN ). MetalNet also successfully recapitulated all known zinc-binding sites from the human spliceosome complex. The pipeline of MetalNet provides a unique and enabling tool for interrogating the hidden metalloproteome and studying metal biology.

Opioid Receptors Modulate Firing and Synaptic Transmission in the Paraventricular Nucleus of the Thalamus.

The paraventricular nucleus of the thalamus (PVT) is involved in drug addiction-related behaviors, and morphine is a widely used opioid for the relief of severe pain. Morphine acts via opioid receptors, but the function of opioid receptors in the PVT has not been fully elucidated. Here, we used in vitro electrophysiology to study neuronal activity and synaptic transmission in the PVT of male and female mice. Activation of opioid receptors suppresses the firing and inhibitory synaptic transmission of PVT neurons in brain slices. On the other hand, the involvement of opioid modulation is reduced after chronic morphine exposure, probably because of desensitization and internalization of opioid receptors in the PVT. Overall, the opioid system is essential for the modulation of PVT activities.SIGNIFICANCE STATEMENT Opioid receptors modulate the activities and synaptic transmission in the PVT by suppressing the firing rate and inhibitory synaptic inputs. These modulations were largely diminished after chronic morphine exposure.

Pan-cancer analysis identifies the IRF family as a biomarker for survival prognosis and immunotherapy.

IRF family genes have been shown to be crucial in tumorigenesis and tumour immunity. However, information about the role of IRF in the systematic assessment of pan-cancer and in predicting the efficacy of tumour therapy is still unknown. In this work, we performed a systematic analysis of IRF family genes in 33 tumour samples, including expression profiles, genomics and clinical characteristics. We then applied Single-Sample Gene-Set Enrichment Analysis (ssGSEA) to calculate IRF-scores and analysed the impact of IRF-scores on tumour progression, immune infiltration and treatment efficacy. Our results showed that genomic alterations, including SNPs, CNVs and DNA methylation, can lead to dysregulation of IRFs expression in tumours and participate in regulating multiple tumorigenesis. IRF-score expression differed significantly between 12 normal and tumour samples and the impact on tumour prognosis and immune infiltration depended on tumour type. IRF expression was correlated to drug sensitivity and to the expression of immune checkpoints and immune cell infiltration, suggesting that dysregulation of IRF family expression may be a critical factor affecting tumour drug response. Our study comprehensively characterizes the genomic and clinical profile of IRFs in pan-cancer and highlights their reliability and potential value as predictive markers of oncology drug efficacy. This may provide new ideas for future personalized oncology treatment.

Elucidating genetic and molecular basis of altered higher-order brain structure-function coupling in major depressive disorder.

Previous studies have shown that major depressive disorder (MDD) patients exhibit structural and functional impairments, but few studies have investigated changes in higher-order coupling between structure and function. Here, we systematically investigated the effect of MDD on higher-order coupling between structural connectivity (SC) and functional connectivity (FC). Each brain region was mapped into embedding vector by the node2vec algorithm. We used support vector machine (SVM) with the brain region embedding vector to distinguish MDD patients from health controls (HCs) and identify the most discriminative brain regions. Our study revealed that MDD patients had decreased higher-order coupling in connections between the most discriminative brain regions and local connections in rich-club organization and increased higher-order coupling in connections between the ventral attentional network and limbic network compared with HCs. Interestingly, transcriptome-neuroimaging association analysis demonstrated the correlations between regional rSC-FC coupling variations between MDD patients and HCs and α/ß-hydrolase domain-containing 6 (ABHD6), ß 1,3-N-acetylglucosaminyltransferase-9(ß3GNT9), transmembrane protein 45B (TMEM45B), the correlation between regional dSC-FC coupling variations and retinoic acid early transcript 1E antisense RNA 1(RAET1E-AS1), and the correlations between regional iSC-FC coupling variations and ABHD6, ß3GNT9, katanin-like 2 protein (KATNAL2). In addition, correlation analysis with neurotransmitter receptor/transporter maps found that the rSC-FC and iSC-FC coupling variations were both correlated with neuroendocrine transporter (NET) expression, and the dSC-FC coupling variations were correlated with metabotropic glutamate receptor 5 (mGluR5). Further mediation analysis explored the relationship between genes, neurotransmitter receptor/transporter and MDD related higher-order coupling variations. These findings indicate that specific genetic and molecular factors underpin the observed disparities in higher-order SC-FC coupling between MDD patients and HCs. Our study confirmed that higher-order coupling between SC and FC plays an important role in diagnosing MDD. The identification of new biological evidence for MDD etiology holds promise for the development of innovative antidepressant therapies.

Triple-negative breast cancer survival prediction using artificial intelligence through integrated analysis of tertiary lymphoid structures and tumor budding.

BACKGROUND: Triple-negative breast cancer (TNBC) is a highly heterogeneous and clinically aggressive disease. Accumulating evidence indicates that tertiary lymphoid structures (TLSs) and tumor budding (TB) are significantly correlated with the outcomes of patients who have TNBC, but no integrated TLS-TB profile has been established to predict their survival. The objective of this study was to investigate the relationship between the TLS/TB ratio and clinical outcomes of patients with TNBC using artificial intelligence (AI)-based analysis. METHODS: The infiltration levels of TLSs and TB were evaluated using hematoxylin and eosin staining, immunohistochemistry staining, and AI-based analysis. Various cellular subtypes within TLS were determined by multiplex immunofluorescence. Subsequently, the authors established a nomogram model, conducted calibration curve analyses, and performed decision curve analyses using R software. RESULTS: In both the training and validation cohorts, the antitumor/protumor model established by the authors demonstrated a positive correlation between the TLS/TB index and the overall survival (OS) and relapse-free survival (RFS) of patients with TNBC. Notably, patients who had a high percentage of CD8-positive T cells, CD45RO-positive T cells, or CD20-positive B cells within the TLSs experienced improved OS and RFS. Furthermore, the authors developed a comprehensive TLS-TB profile nomogram based on the TLS/TB index. This novel model outperformed the classical tumor-lymph node-metastasis staging system in predicting the OS and RFS of patients with TNBC. CONCLUSIONS: A novel strategy for predicting the prognosis of patients with TNBC was established through integrated AI-based analysis and a machine-learning workflow. The TLS/TB index was identified as an independent prognostic factor for TNBC. This nomogram-based TLS-TB profile would help improve the accuracy of predicting the prognosis of patients who have TNBC.

Abundance of Modifications in Mature miRNAs Revealed by LC-MS/MS Method Coupled with a Two-Step Hybridization Purification Strategy.

Accurate detection of endogenous miRNA modifications, such as N6-methyladenosine (m6A), 7-methylguanosine (m7G), and 5-methylcytidine (m5C), poses significant challenges, resulting in considerable uncertainty regarding their presence in mature miRNAs. In this study, we demonstrate for the first time that liquid chromatography coupled with a tandem mass spectrometry (LC-MS/MS) nucleoside analysis method is a practical tool for quantitatively analyzing human miRNA modifications. The newly designed liquid-solid two-step hybridization (LSTH) strategy enhances specificity for miRNA purification, while LC-MS/MS offers robust capability in recognizing modifications and sufficient sensitivity with detection limits ranging from attomoles to low femtomoles. Therefore, it provides a more reliable approach compared to existing techniques for revealing modifications in endogenous miRNAs. With this approach, we characterized m6A, m7G, and m5C modifications in miR-21-5p, Let-7a/e-5p, and miR-10a-5p isolated from cultured cells and observed unexpectedly low abundance (<1% at each site) of these modifications.

Towards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning approach.

MOTIVATION: Automated extraction of participants, intervention, comparison/control, and outcome (PICO) from the randomized controlled trial (RCT) abstracts is important for evidence synthesis. Previous studies have demonstrated the feasibility of applying natural language processing (NLP) for PICO extraction. However, the performance is not optimal due to the complexity of PICO information in RCT abstracts and the challenges involved in their annotation. RESULTS: We propose a two-step NLP pipeline to extract PICO elements from RCT abstracts: (i) sentence classification using a prompt-based learning model and (ii) PICO extraction using a named entity recognition (NER) model. First, the sentences in abstracts were categorized into four sections namely background, methods, results, and conclusions. Next, the NER model was applied to extract the PICO elements from the sentences within the title and methods sections that include >96% of PICO information. We evaluated our proposed NLP pipeline on three datasets, the EBM-NLPmoddataset, a randomly selected and reannotated dataset of 500 RCT abstracts from the EBM-NLP corpus, a dataset of 150 COVID-19 RCT abstracts, and a dataset of 150 Alzheimer's disease (AD) RCT abstracts. The end-to-end evaluation reveals that our proposed approach achieved an overall micro F1 score of 0.833 on the EBM-NLPmod dataset, 0.928 on the COVID-19 dataset, and 0.899 on the AD dataset when measured at the token-level and an overall micro F1 score of 0.712 on EBM-NLPmod dataset, 0.850 on the COVID-19 dataset, and 0.805 on the AD dataset when measured at the entity-level. AVAILABILITY: Our codes and datasets are publicly available at https://github.com/BIDS-Xu-Lab/section_specific_annotation_of_PICO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The impact of diabetes mellitus on cardiac function assessed by magnetic resonance imaging in patients with hypertrophic cardiomyopathy.

BACKGROUND: The adverse prognostic impact of diabetes on hypertrophic cardiomyopathy (HCM) is poorly understood. We sought to explore the underlying mechanisms in terms of structural and functional remodelling in HCM patients with coexisting diabetes (HCM-DM). METHODS: A total of 45 HCM-DM patients were retrospectively included. Isolated HCM controls (HCM patients without diabetes) were matched to HCM-DM patients in terms of maximal wall thickness, age, and gender distribution. Left ventricular (LV) and atrial (LA) performance were evaluated using cardiac magnetic resonance feature tracking strain analyses. The associations between diabetes and LV/LA impairment were investigated by univariable and multivariable linear regression. RESULTS: Compared with the isolated HCM controls, the HCM-DM patients had smaller end-diastolic volume and stroke volume, lower ejection fraction, larger mass/volume ratio and impaired strains in all three directions (all P < 0.05). In terms of the LA parameters, HCM-DM patients presented impaired LA reservoir and conduit strain/strain rate (all P < 0.05). Among all HCM patients, comorbidity with diabetes was independently associated with a low LV ejection fraction (ß = - 6.05, P < 0.001) and impaired global longitudinal strain (ß = 1.40, P = 0.007). Moreover, compared with the isolated HCM controls, HCM-DM patients presented with more myocardial fibrosis according to late gadolinium enhancement, which was an independent predictor of impaired LV global radial strain (ß = - 45.81, P = 0.008), LV global circumferential strain (ß = 18.25, P = 0.003), LA reservoir strain (ß = - 59.20, P < 0.001) and strain rate (ß = - 2.90, P = 0.002). CONCLUSIONS: Diabetes has adverse effects on LV and LA function in HCM patients, which may be important contributors to severe manifestations and outcomes in those patients. The present study strengthened the evidence of the prevention and management of diabetes in HCM patients.

Glycemic control and clinical outcomes in diabetic patients with heart failure and reduced ejection fraction: insight from ventricular remodeling using cardiac MRI.

BACKGROUND: Glycemic control, as measured by glycosylated hemoglobin (HbA1c), is an important biomarker to evaluate diabetes severity and is believed to be associated with heart failure development. Type 2 diabetes mellitus (T2DM) and heart failure with reduced ejection fraction (HFrEF) commonly coexist, and the combination of these two diseases indicates a considerably poorer outcome than either disease alone. Therefore, glycemic control should be carefully managed. The present study aimed to explore the association between glycemic control and clinical outcomes, and to determine the optimal glycemic target in this specific population. METHODS: A total of 262 patients who underwent cardiac MRI were included and were split by HbA1c levels [HbA1c < 6.5% (intensive control), HbA1c 6.5-7.5% (modest control), and HbA1c > 7.5% (poor control)]. The biventricular volume and function, as well as left ventricular (LV) systolic strains in patients in different HbA1c categories, were measured and compared. The primary and secondary outcomes were recorded. The association of different HbA1c levels with adverse outcomes was assessed. RESULTS: Despite similar biventricular ejection fractions, both patients with intensive and poor glycemic control exhibited prominent deterioration of LV systolic strain in the longitudinal component (P = 0.004). After a median follow-up of 35.0 months, 55 patients (21.0%) experienced at least one confirmed endpoint event. Cox multivariable analysis indicated that both patients in the lowest and highest HbA1c categories exhibited a more than 2-fold increase in the risk for primary outcomes [HbA1c < 6.5%: hazard ratio (HR) = 2.42, 95% confidence interval (CI) = 1.07-5.45; P = 0.033; HbA1c > 7.5%: HR = 2.24, 95% CI = 1.01-4.99; P = 0.038] and secondary outcomes (HbA1c < 6.5%: HR = 2.84, 95% CI = 1.16-6.96; P = 0.022; HbA1c > 7.5%: HR = 2.65, 95% CI = 1.08-6.50; P = 0.038) compared with those in the middle HbA1c category. CONCLUSIONS: We showed a U-shaped association of glycemic control with clinical outcomes in patients with T2DM and HFrEF, with the lowest risk of adverse outcomes among patients with modest glycemic control. HbA1c between 6.5% and 7.5% may be served as the optimal hypoglycemic target in this specific population.

Early left ventricular microvascular dysfunction in diabetic pigs: a longitudinal quantitative myocardial perfusion CMR study.

BACKGROUND: Microvascular pathology is one of the main characteristics of diabetic cardiomyopathy; however, the early longitudinal course of diabetic microvascular dysfunction remains uncertain. This study aimed to investigate the early dynamic changes in left ventricular (LV) microvascular function in diabetic pig model using the cardiac magnetic resonance (CMR)-derived quantitative perfusion technique. METHODS: Twelve pigs with streptozotocin-induced diabetes mellitus (DM) were included in this study, and longitudinal CMR scanning was performed before and 2, 6, 10, and 16 months after diabetic modeling. CMR-derived semiquantitative parameters (upslope, maximal signal intensity, perfusion index, and myocardial perfusion reserve index [MPRI]) and fully quantitative perfusion parameters (myocardial blood flow [MBF] and myocardial perfusion reserve [MPR]) were analyzed to evaluate longitudinal changes in LV myocardial microvascular function. Pearson correlation was used to analyze the relationship between LV structure and function and myocardial perfusion function. RESULTS: With the progression of DM duration, the upslope at rest showed a gradually increasing trend (P = 0.029); however, the upslope at stress and MBF did not change significantly (P > 0.05). Regarding perfusion reserve function, both MPRI and MPR showed a decreasing trend with the progression of disease duration (MPRI, P = 0.001; MPR, P = 0.042), with high consistency (r = 0.551, P < 0.001). Furthermore, LV MPR is moderately associated with LV longitudinal strain (r = - 0.353, P = 0.022), LV remodeling index (r = - 0.312, P = 0.033), fasting blood glucose (r = - 0.313, P = 0.043), and HbA1c (r = - 0.309, P = 0.046). Microscopically, pathological results showed that collagen volume fraction increased gradually, whereas no significant decrease in microvascular density was observed with the progression of DM duration. CONCLUSIONS: Myocardial microvascular reserve function decreased gradually in the early stage of DM, which is related to both structural (but not reduced microvascular density) and functional abnormalities of microvessels, and is associated with increased blood glucose, reduced LV deformation, and myocardial remodeling.

Reduced thoracic skeletal muscle size is associated with adverse outcomes in diabetes patients with heart failure and reduced ejection fraction: quantitative analysis of sarcopenia by using cardiac MRI.

BACKGROUND: Sarcopenia is frequently found in patients with heart failure with reduced ejection fraction (HFrEF) and is associated with reduced exercise capacity, poor quality of life and adverse outcomes. Recent evidence suggests that axial thoracic skeletal muscle size could be used as a surrogate to assess sarcopenia in HFrEF. Since diabetes mellitus (DM) is one of the most common comorbidities with HFrEF, we aimed to explore the potential association of axial thoracic skeletal muscle size with left ventricular (LV) remodeling and determine its prognostic significance in this condition. METHODS: A total of 243 diabetes patients with HFrEF were included in this study. Bilateral axial thoracic skeletal muscle size was obtained using cardiac MRI. Patients were stratified by the tertiles of axial thoracic skeletal muscle index (SMI). LV structural and functional indices, as well as amino-terminal pro-B-type natriuretic peptide (NT-proBNP), were measured. The determinants of elevated NT-proBNP were assessed using linear regression analysis. The associations between thoracic SMI and clinical outcomes were assessed using a multivariable Cox proportional hazards model. RESULTS: Patients in the lowest tertile of thoracic SMI displayed a deterioration in LV systolic strain in three components, together with an increase in LV mass and a heavier burden of myocardial fibrosis (all P < 0.05). Moreover, thoracic SMI (ß = -0.25; P < 0.001), rather than body mass index (ß = -0.04; P = 0.55), was independently associated with the level of NT-proBNP. The median follow-up duration was 33.6 months (IQR, 20.4-52.8 months). Patients with adverse outcomes showed a lower thoracic SMI (40.1 [34.3, 47.9] cm2/m2 vs. 45.3 [37.3, 55.0] cm2/m2; P < 0.05) but a similar BMI (P = 0.76) compared with those without adverse outcomes. A higher thoracic SMI indicated a lower risk of adverse outcomes (hazard ratio: 0.96; 95% confidence interval: 0.92-0.99; P = 0.01). CONCLUSIONS: With respect to diabetes patients with HFrEF, thoracic SMI is a novel alternative for evaluating muscle wasting in sarcopenia that can be obtained by a readily available routine cardiac MRI protocol. A reduction in thoracic skeletal muscle size predicts poor outcomes in the context of DM with HFrEF.