In-silico identification of phenotype-biased functional modules

<p>Abstract</p> <p>Background</p> <p>Phenotypes exhibited by microorganisms can be useful for several purposes, e.g., ethanol as an alternate fuel. Sometimes, the target phenotype maybe required in combination with other phenotypes, in order to be useful, for e.g., an industrial process may require that the organism survive in an anaerobic, alcohol rich environment and be able to feed on both hexose and pentose sugars to produce ethanol. This combination of traits may not be available in any existing organism or if they do exist, the mechanisms involved in the phenotype-expression may not be efficient enough to be useful. Thus, it may be required to genetically modify microorganisms. However, before any genetic modification can take place, it is important to identify the underlying cellular subsystems responsible for the expression of the target phenotype.</p> <p>Results</p> <p>In this paper, we develop a method to identify statistically significant and phenotypically-biased functional modules. The method can compare the organismal network information from hundreds of phenotype expressing and phenotype non-expressing organisms to identify cellular subsystems that are more prone to occur in phenotype-expressing organisms than in phenotype non-expressing organisms. We have provided literature evidence that the phenotype-biased modules identified for phenotypes such as hydrogen production (dark and light fermentation), respiration, gram-positive, gram-negative and motility, are indeed phenotype-related.</p> <p>Conclusion</p> <p>Thus we have proposed a methodology to identify phenotype-biased cellular subsystems. We have shown the effectiveness of our methodology by applying it to several target phenotypes. The code and all supplemental files can be downloaded from (<url>http://freescience.org/cs/phenotype-biased-biclusters/</url>).</p

The Mechanism of Downregulated Interstitial Fluid Drainage Following Neuronal Excitation.

The drainage of brain interstitial fluid (ISF) has been observed to slow down following neuronal excitation, although the mechanism underlying this phenomenon is yet to be elucidated. In searching for the changes in the brain extracellular space (ECS) induced by electrical pain stimuli in the rat thalamus, significantly decreased effective diffusion coefficient (DECS) and volume fraction (α) of the brain ECS were shown, accompanied by the slowdown of ISF drainage. The morphological basis for structural changes in the brain ECS was local spatial deformation of astrocyte foot processes following neuronal excitation. We further studied aquaporin-4 gene (APQ4) knockout rats in which the changes of the brain ECS structure were reversed and found that the slowed DECS and ISF drainage persisted, confirming that the down-regulation of ISF drainage following neuronal excitation was mainly attributable to the release of neurotransmitters rather than to structural changes of the brain ECS. Meanwhile, the dynamic changes in the DECS were synchronized with the release and elimination processes of neurotransmitters following neuronal excitation. In conclusion, the downregulation of ISF drainage following neuronal excitation was found to be caused by the restricted diffusion in the brain ECS, and DECS mapping may be used to track the neuronal activity in the deep brain

Reduced SV2A and GABAA_A receptor levels in the brains of type 2 diabetic rats revealed by [18^{18}F]SDM-8 and [18^{18}F]flumazenil PET

PURPOSE: Type 2 diabetes mellitus (T2DM) is associated with a greater risk of Alzheimer's disease. Synaptic impairment and protein aggregates have been reported in the brains of T2DM models. Here, we assessed whether neurodegenerative changes in synaptic vesicle 2 A (SV2A), γ-aminobutyric acid type A (GABA$_A$) receptor, amyloid-β, tau and receptor for advanced glycosylation end product (RAGE) can be detected in vivo in T2DM rats. Methods: Positron emission tomography (PET) using [$^{18}$F]SDM-8 (SV2A), [$^{18}$F]flumazenil (GABA$_A$ receptor), [$^{18}$F]florbetapir (amyloid-β), [$^{18}$F]PM-PBB3 (tau), and [$^{18}$F]FPS-ZM1 (RAGE) was carried out in 12-month-old diabetic Zucker diabetic fatty (ZDF) and SpragueDawley (SD) rats. Immunofluorescence staining, Thioflavin S staining, proteomic profiling and pathway analysis were performed on the brain tissues of ZDF and SD rats. Results: Reduced cortical [$^{18}$F]SDM-8 uptake and cortical and hippocampal [$^{18}$F]flumazenil uptake were observed in 12-month-old ZDF rats compared to SD rats. The regional uptake of [$^{18}$F]florbetapir and [$^{18}$F]PM-PBB3 was comparable in the brains of 12-month-old ZDF and SD rats. Immunofluorescence staining revealed Thioflavin S-negative, phospho-tau-positive inclusions in the cortex and hypothalamus in the brains of ZDF rats and the absence of amyloid-beta deposits. The level of GABA$_A$ receptors was lower in the cortex of ZDF rats than SD rats. Proteomic analysis further demonstrated that, compared with SD rats, synaptic-related proteins and pathways were downregulated in the hippocampus of ZDF rats. Conclusion: These findings provide in vivo evidence for regional reductions in SV2A and GABA$_A$ receptor levels in the brains of aged T2DM ZDF rats

Uncovering distinct progression patterns of tau deposition in progressive supranuclear palsy using [18F]Florzolotau PET imaging and subtype/stage inference algorithm.

BACKGROUND Progressive supranuclear palsy (PSP) is a primary 4-repeat tauopathy with diverse clinical phenotypes. Previous post-mortem studies examined tau deposition sequences in PSP, but in vivo scrutiny is lacking. METHODS We conducted [18F]Florzolotau tau positron emission tomography (PET) scans on 148 patients who were clinically diagnosed with PSP and 20 healthy controls. We employed the Subtype and Stage Inference (SuStaIn) algorithm to identify PSP subtype/stage and related tau patterns, comparing clinical features across subtypes and assessing PSP stage-clinical severity association. We also evaluated functional connectivity differences among subtypes through resting-state functional magnetic resonance imaging. FINDINGS We identified two distinct subtypes of PSP: Subtype1 and Subtype2. Subtype1 typically exhibits a sequential progression of the disease, starting from subcortical and gradually moving to cortical regions. Conversely, Subtype2 is characterized by an early, simultaneous onset in both regions. Interestingly, once the disease is initiated, Subtype1 tends to spread more rapidly within each region compared to Subtype2. Individuals categorized as Subtype2 are generally older and exhibit less severe dysfunctions in areas such as cognition, bulbar, limb motor, and general motor functions compared to those with Subtype1. Moreover, they have a more favorable prognosis in terms of limb motor function. We found significant correlations between several clinical variables and the identified PSP SuStaIn stages. Furthermore, Subtype2 displayed a remarkable reduction in functional connectivity compared to Subtype1. INTERPRETATION We present the evidence of distinct in vivo spatiotemporal tau trajectories in PSP. Our findings can contribute to precision medicine advancements for PSP. FUNDING This work was supported by grants from the National Natural Science Foundation of China (number 82272039, 81971641, 82021002, and 92249302); Swiss National Science Foundation (number 188350); the STI2030-Major Project of China (number 2022ZD0211600); the Clinical Research Plan of Shanghai Hospital Development Center of China (number SHDC2020CR1038B); and the National Key R&D Program of China (number 2022YFC2009902, 2022YFC2009900), the China Scholarship Council (number 202006100181); the Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy, ID 390857198)

A Left Ventricular Mechanical Dyssynchrony-Based Nomogram for Predicting Major Adverse Cardiac Events Risk in Patients With Ischemia and No Obstructive Coronary Artery Disease.

Background The risk stratification of patients with ischemia and no obstructive coronary artery disease (INOCA) remains suboptimal. This study aims to establish a left ventricular mechanical dyssynchrony (LVMD)-based nomogram to improve the present situation. Methods Patients with suspected coronary artery disease (CAD) were retrospectively enrolled and divided into three groups: normal (stenosis 4, summed difference score ≥2), and obstructive CAD (stenosis ≥50%). LVMD was defined by ROC analysis. INOCA group were followed up for the occurrence of major adverse cardiac events (MACEs: cardiovascular death, non-fatal myocardial infarction, revascularization, stroke, heart failure, and hospitalization for unstable angina). Nomogram was established using multivariate Cox regression analysis. Results Among 334 patients (118 [35.3%] INOCA), LVMD parameters were significantly higher in INOCA group versus normal group but they did not differ between obstructive CAD groups. In INOCA group, 27 (22.9%) MACEs occurred during a 26-month median follow-up. Proportion of LVMD was significantly higher with MACEs under both stress (63.0% vs. 22.0%, P < 0.001) and rest (51.9% vs. 20.9%, P = 0.002). Kaplan-Meier analysis revealed significantly higher rate of MACEs (stress log-rank: P = 0.002; rest log-rank: P < 0.001) in LVMD patients. Multivariate Cox regression analysis showed that stress LVMD (HR: 3.82; 95% CI: 1.30-11.20; P = 0.015) was an independent predictor of MACEs. The internal bootstrap resampling approach indicates that the C-index of nomogram was 0.80 (95% CI: 0.71-0.89) and the AUC values for 1 and 3 years of risk prediction were 0.68 (95% CI: 0.46-0.89) and 0.84 (95% CI: 0.72-0.95), respectively. Conclusion LVMD-based nomogram might provide incremental prognostic value and improve the risk stratification in INOCA patients

Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction.

Despite the potential of deep learning (DL)-based methods in substituting CT-based PET attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their limited capability in handling large heterogeneity of tracers and scanners of PET imaging. This study employs a simple way to integrate domain knowledge in DL for CT-free PET imaging. In contrast to conventional direct DL methods, we simplify the complex problem by a domain decomposition so that the learning of anatomy-dependent attenuation correction can be achieved robustly in a low-frequency domain while the original anatomy-independent high-frequency texture can be preserved during the processing. Even with the training from one tracer on one scanner, the effectiveness and robustness of our proposed approach are confirmed in tests of various external imaging tracers on different scanners. The robust, generalizable, and transparent DL development may enhance the potential of clinical translation

Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.

PURPOSE This work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential diagnosis of parkinsonism. METHODS This study involved 1017 subjects who underwent DAT PET imaging ([11C]CFT) including 43 healthy subjects and 974 parkinsonian patients with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA) or progressive supranuclear palsy (PSP). We developed a 3D deep convolutional neural network to learn distinguishable DAT features for the differential diagnosis of parkinsonism. A full-gradient saliency map approach was employed to investigate the functional basis related to the decision mechanism of the network. Furthermore, deep-learning-guided radiomics features and quantitative analysis were compared with their conventional counterparts to further interpret the performance of deep learning. RESULTS The proposed network achieved area under the curve of 0.953 (sensitivity 87.7%, specificity 93.2%), 0.948 (sensitivity 93.7%, specificity 97.5%), and 0.900 (sensitivity 81.5%, specificity 93.7%) in the cross-validation, together with sensitivity of 90.7%, 84.1%, 78.6% and specificity of 88.4%, 97.5% 93.3% in the blind test for the differential diagnosis of IPD, MSA and PSP, respectively. The saliency map demonstrated the most contributed areas determining the diagnosis located at parkinsonism-related regions, e.g., putamen, caudate and midbrain. The deep-learning-guided binding ratios showed significant differences among IPD, MSA and PSP groups (P < 0.001), while the conventional putamen and caudate binding ratios had no significant difference between IPD and MSA (P = 0.24 and P = 0.30). Furthermore, compared to conventional radiomics features, there existed average above 78.1% more deep-learning-guided radiomics features that had significant differences among IPD, MSA and PSP. CONCLUSION This study suggested the developed deep neural network can decode in-depth information from DAT and showed potential to assist the differential diagnosis of parkinsonism. The functional regions supporting the diagnosis decision were generally consistent with known parkinsonian pathology but provided more specific guidance for feature selection and quantitative analysis

Differential diagnosis of parkinsonism based on deep metabolic imaging indices.

The clinical presentations of early idiopathic Parkinson's disease (PD) substantially overlap with those of atypical parkinsonian syndromes like multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). This study aimed to develop metabolic imaging indices based on deep learning to support the differential diagnosis of these conditions. Methods: A benchmark Huashan parkinsonian PET imaging (HPPI, China) database including 1275 parkinsonian patients and 863 non-parkinsonian subjects with 18F-FDG PET images was established to support artificial intelligence development. A 3D deep convolutional neural network was developed to extract deep metabolic imaging (DMI) indices, which was blindly evaluated in an independent cohort with longitudinal follow-up from the HPPI, and an external German cohort of 90 parkinsonian patients with different imaging acquisition protocols. Results: The proposed DMI indices had less ambiguity space in the differential diagnosis. They achieved sensitivities of 98.1%, 88.5%, and 84.5%, and specificities of 90.0%, 99.2%, and 97.8% for the diagnosis of PD, MSA, and PSP in the blind test cohort. In the German cohort, They resulted in sensitivities of 94.1%, 82.4%, 82.1%, and specificities of 84.0%, 99.9%, 94.1% respectively. Employing the PET scans independently achieved comparable performance to the integration of demographic and clinical information into the DMI indices. Conclusion: The DMI indices developed on the HPPI database show potential to provide an early and accurate differential diagnosis for parkinsonism and is robust when dealing with discrepancies between populations and imaging acquisitions

Functional Annotation of Hierarchical Modularity

In biological networks of molecular interactions in a cell, network motifs that are biologically relevant are also functionally coherent, or form functional modules. These functionally coherent modules combine in a hierarchical manner into larger, less cohesive subsystems, thus revealing one of the essential design principles of system-level cellular organization and function–hierarchical modularity. Arguably, hierarchical modularity has not been explicitly taken into consideration by most, if not all, functional annotation systems. As a result, the existing methods would often fail to assign a statistically significant functional coherence score to biologically relevant molecular machines. We developed a methodology for hierarchical functional annotation. Given the hierarchical taxonomy of functional concepts (e.g., Gene Ontology) and the association of individual genes or proteins with these concepts (e.g., GO terms), our method will assign a Hierarchical Modularity Score (HMS) to each node in the hierarchy of functional modules; the HMS score and its p{value measure functional coherence of each module in the hierarchy. While existing methods annotate each module with a set of ‘‘enriched’ ’ functional terms in a bag of genes, our complementary method provides the hierarchical functional annotation of the modules and their hierarchically organized components. A hierarchical organization of functional modules often comes as a bi-product of cluster analysis of gene expression data or protein interaction data. Otherwise, our method will automatically build such a hierarchy by directly incorporating the functional taxonomy information into the hierarchy search process and by allowin