Dissecting the shared genetic architecture between Alzheimer's disease and frailty: a cross-trait meta-analyses of genome-wide association studies.

Introduction: Frailty is the most common medical condition affecting the aging population, and its prevalence increases in the population aged 65 or more. Frailty is commonly diagnosed using the frailty index (FI) or frailty phenotype (FP) assessments. Observational studies have indicated the association of frailty with Alzheimer's disease (AD). However, the shared genetic and biological mechanism of these comorbidity has not been studied. Methods: To assess the genetic relationship between AD and frailty, we examined it at single nucleotide polymorphism (SNP), gene, and pathway levels. Results: Overall, 16 genome-wide significant loci (15 unique loci) (p meta-analysis < 5 × 10-8) and 22 genes (21 unique genes) were identified between AD and frailty using cross-trait meta-analysis. The 8 shared loci implicated 11 genes: CLRN1-AS1, CRHR1, FERMT2, GRK4, LINC01929, LRFN2, MADD, RP11-368P15.1, RP11-166N6.2, RNA5SP459, and ZNF652 between AD and FI, and 8 shared loci between AD and FFS implicated 11 genes: AFF3, C1QTNF4, CLEC16A, FAM180B, FBXL19, GRK4, LINC01104, MAD1L1, RGS12, ZDHHC5, and ZNF521. The loci 4p16.3 (GRK4) was identified in both meta-analyses. The colocalization analysis supported the results of our meta-analysis in these loci. The gene-based analysis revealed 80 genes between AD and frailty, and 4 genes were initially identified in our meta-analyses: C1QTNF4, CRHR1, MAD1L1, and RGS12. The pathway analysis showed enrichment for lipoprotein particle plasma, amyloid fibril formation, protein kinase regulator, and tau protein binding. Conclusion: Overall, our results provide new insights into the genetics of AD and frailty, suggesting the existence of non-causal shared genetic mechanisms between these conditions.

Genetic overlap between Alzheimer's disease and immune-mediated diseases: an atlas of shared genetic determinants and biological convergence.

The occurrence of immune disease comorbidities in Alzheimer's disease (AD) has been observed in both epidemiological and molecular studies, suggesting a neuroinflammatory basis in AD. However, their shared genetic components have not been systematically studied. Here, we composed an atlas of the shared genetic associations between 11 immune-mediated diseases and AD by analyzing genome-wide association studies (GWAS) summary statistics. Our results unveiled a significant genetic overlap between AD and 11 individual immune-mediated diseases despite negligible genetic correlations, suggesting a complex shared genetic architecture distributed across the genome. The shared loci between AD and immune-mediated diseases implicated several genes, including GRAMD1B, FUT2, ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9, and TNIP1, all of which are protein-coding genes and thus potential drug targets. Top biological pathways enriched with these identified shared genes were related to the immune system and cell adhesion. In addition, in silico single-cell analyses showed enrichment of immune and brain cells, including neurons and microglia. In summary, our results suggest a genetic relationship between AD and the 11 immune-mediated diseases, pinpointing the existence of a shared however non-causal genetic basis. These identified protein-coding genes have the potential to serve as a novel path to therapeutic interventions for both AD and immune-mediated diseases and their comorbidities.

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach.

Background: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points. Objective: To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach. Methods: We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. Results: We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Conclusions: Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.

Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: an integrative study of single-nucleus transcriptomes and genetic association.

BACKGROUND: Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains. METHODS: We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database. RESULTS: We identified 190 dysregulated LR interactions across six major cell types in AD PFC, of which 107 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in the astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 44 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport,' among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs. CONCLUSIONS: Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.

Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: An integrative study of single-nucleus transcriptomes and genetic association.

Background: Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains. Methods: We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database. Results: We identified 316 dysregulated LR interactions across six major cell types in AD PFC, of which 210 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 60 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport', among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs. Conclusions: Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.

Genetic overlap between Alzheimer's disease and immune-mediated diseases: An atlas of shared genetic determinants and biological convergence.

The occurrence of immune disease comorbidities in Alzheimer's disease (AD) has been observed in both epidemiological and molecular studies, suggesting a neuroinflammatory basis in AD. However, their shared genetic components have not been systematically studied. Here, we composed an atlas of the shared genetic associations between 11 immune-mediated diseases and AD by analyzing genome-wide association studies (GWAS) summary statistics. Our results unveiled a significant genetic overlap between AD and 11 individual immune-mediated diseases despite negligible genetic correlations, suggesting a complex shared genetic architecture distributed across the genome. The shared loci between AD and immune-mediated diseases implicated several genes, including GRAMD1B, FUT2, ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9 and TNIP1, all of which are protein-coding genes and thus potential drug targets. Top biological pathways enriched with these identified shared genes were related to the immune system and cell adhesion. In addition, in silico single-cell analyses showed enrichment of immune and brain cells, including neurons and microglia. In summary, our results suggest a genetic relationship between AD and the 11 immune-mediated diseases, pinpointing the existence of a shared however non-causal genetic basis. These identified protein-coding genes have the potential to serve as a novel path to therapeutic interventions for both AD and immune-mediated diseases and their comorbidities.

Disentangling accelerated cognitive decline from the normal aging process and unraveling its genetic components: A neuroimaging-based deep learning approach.

Background: The progressive cognitive decline that is an integral component of AD unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and Alzheimer's disease between different chronological points. Methods: We developed a deep-learning framework based on dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. Results: We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G>T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neuron and plays a role in controlling cell growth and differentiation. In addition, MUC7 and PROL1/OPRPNon chromosome 4 were significant at the gene level. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Furthermore, we found that the cognitive decline slope GWAS was positively correlated with previous AD GWAS. Conclusion: Our deep learning model was demonstrated effective on extracting relevant neuroimaging features and predicting individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene. Our approach has the potential to disentangle accelerated cognitive decline from the normal aging process and to determine its related genetic factors, leveraging opportunities for early intervention.

The kynurenine pathway in major depressive disorder, bipolar disorder, and schizophrenia: a systematic review and meta-analysis of cerebrospinal fluid studies

Objectives: The kynurenine (KYN) pathway has been attracting attention as a relevant pathway in schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). We conducted a systematic review and meta-analysis of studies examining KYN pathway metabolites from cerebrospinal fluid (CSF) samples in SZ, BD, and MDD. Methods: The PubMed and Scopus databases were systematically searched to identify peer-reviewed case-control studies published until April 2022 that assessed KYN metabolites, namely, tryptophan (TRP), KYN, kynurenic acid (KA), quinolinic acid (QA), and 3-hydroxykynurenine (3-HK), in subjects with SZ, BD, or MDD compared with healthy controls (HC). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. The random effects model method was selected for comparison of standardized mean differences (SMD) between two groups. Results: Twenty-three articles met the inclusion criteria (k = 8, k = 8, k = 11, for SZ, BD, and MDD, respectively). In SZ, KA levels were increased (SMD = 2.64, confidence interval [CI] = 1.16 to 4.13, p = 0.0005, I2 = 96%, k = 6, n=384). TRP (k = 5) and KYN (k = 4) did not differ significantly. In BD, TRP levels (k = 7) did not differ significantly. The level of KA was increased in MDD (k = 2), but the small number of studies precluded evaluation of statistical significance. Finally, in MDD, although some studies tended to show an increased level of KYN in those with remission vs. decreased levels in those with current depression, no significant difference was found in any KYN metabolite levels. Similarly, an increased level of QA was found, but the number of studies (k = 2) was small. Conclusion: KA, which has possibly neuroprotective effects, is increased in SZ. QA, which has neurotoxic effects, may be increased in MDD. There were no alterations in BD. Alterations in the KYN pathway may occur based on population characteristics and mood states. Future studies should explore the utility of these metabolites as biomarkers.

Genetically-regulated pathway-polygenic risk score (GRPa-PRS): A risk stratification method to identify genetically regulated pathways in polygenic diseases.

Background: Alzheimer's disease (AD) is a common neurodegenerative disease in the elderly population, with genetic factors playing an important role. A considerable proportion of elderly people carry a high genetic AD risk but evade AD. On the other hand, some individuals with a low risk for AD eventually develop AD. We hypothesized that unknown counterfactors might be involved in reversing the polygenic risk scores (PRS) prediction, which might provide insights into AD pathogenesis, prevention, and early clinical intervention. Methods: We built a novel computational framework to identify genetically-regulated pathways (GRPa) using PRS-based stratification for each cohort. We curated two AD cohorts with genotyping data; the discovery and the replication dataset include 2722 and 2492 individuals, respectively. First, we calculated the optimized PRS model based on the three latest AD GWAS summary statistics for each cohort. Then, we sub-grouped the individuals by their PRS and clinical diagnosis into groups such as cognitively normal (CN) with high PRS for AD (resilient group), AD cases with low PRS (susceptible group), and AD/CNs participants with similar PRS backgrounds. Lastly, we imputed the individual genetically-regulated expression (GReX) and identified the differential GRPas between subgroups with gene-set enrichment analysis and gene-set variational analysis in 2 models with and without the effect of APOE. Results: For each subgroup, we conducted the same procedures in both the discovery and replication datasets across three PRS models for comparison. In Model 1 with the APOE region, we identified well-known AD-related pathways, including amyloid-beta clearance, tau protein binding, and astrocytes response to oxidative stress. In Model 2 without the APOE region, synapse function, microglia function, histidine metabolism, and thiolester hydrolase activity were significant, suggesting that they are pathways independent of the effect of APOE. Finally, our GRPa-PRS method reduces the false discovery rate in detecting differential pathways compared to another variants-based pathway PRS method. Conclusions: We developed a framework, GRPa-PRS, to systematically explore the differential GRPas among individuals stratified by their estimated PRS. The GReX-level comparison among those groups unveiled new insights into the pathways associated with AD risk and resilience. Our framework can be extended to other polygenic complex diseases.

The kynurenine pathway in major depressive disorder, bipolar disorder, and schizophrenia: a systematic review and meta-analysis of cerebrospinal fluid studies.

INTRODUCTION: The kynurenine pathway has been attracting attention as a relevant pathway in schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). We conducted a systematic review and meta-analysis of the kynurenine pathway metabolites from cerebrospinal fluid (CSF) samples in SZ, BD, and MDD. METHODS: PubMed and Scopus databases were systematically searched to identify peer-reviewed case-control studies until April 2022 that assessed kynurenine metabolites, namely, tryptophan (TRP), kynurenine (KYN), kynurenic acid (KA), quinolinic acid (QA), and 3- hydroxykynurenine (3-HK) in SZ, BD, or MDD subjects compared with healthy controls (HC). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. The random effects model method was selected when comparing the standardized mean differences (SMD) between two groups. RESULTS: There were 23 articles that met the inclusion criteria (k=8, k=8, k=11, for SZ, BD, and MDD, respectively). In SZ, KA levels were increased [SMD=2.64, confidence interval (CI) =1.16 to 4.13, p=0.0005, I2=96%, k=6, n=384 subjects]. TRP (k=5) and KYN (k=4) did not differ significantly. In BD, TRP levels (k=7) did not differ significantly. The level of KA was increased in MDD (k=2), but the small number of studies made not possible for statistical significance evaluation. Finally, in MDD, although some studies tended to have an increased level of KYN in those with remission versus decreased levels in those with current depression, no significant difference was found in any of the kynurenine metabolite levels. Similarly, there was an increased level of QA (k=2) but the number of studies (k= 2) was small. CONCLUSION: KA, which has possibly neuroprotective effects, is increased in SZ. QA, which has neurotoxic effects, may be increased in MDD. There were no alterations in BD. There may be alterations in this pathway based on population characteristics and mood states. Future studies should explore the utility of these metabolites as biomarkers.

The kynurenine pathway in Alzheimer's disease: a meta-analysis of central and peripheral levels.

OBJECTIVE: Changes in the kynurenine pathway are recognized in psychiatric disorders, but their role in Alzheimer's disease (AD) is less clear. We aimed to conduct a systematic review and meta-analysis to determine whether tryptophan and kynurenine pathway metabolites are altered in AD. METHODS: We performed a systematic review and random-effects meta-analyses. Inclusion criteria were studies that compared AD and cognitively normal (CN) groups and assessed tryptophan or kynurenine pathway metabolites in cerebrospinal fluid or peripheral blood. RESULTS: Twenty-two studies with a total of 1,356 participants (664 with AD and 692 CN individuals) were included. Tryptophan was decreased only in peripheral blood. The kynurenine-to-tryptophan ratio was only increased in peripheral blood of the AD group. 3-Hydroxykynurenine was decreased only in cerebrospinal fluid and showed higher variability in the CN group than the AD group. Kynurenic acid was increased in cerebrospinal fluid and decreased in peripheral blood. Finally, there were no changes in kynurenine and quinolinic acid between the groups. CONCLUSIONS: Our results suggested a shift toward the kynurenine pathway in both the brain and in the periphery, as well as a shift towards increased kynurenic acid production in the brain but decreased production in peripheral blood. In addition, our analysis indicated dissociation between the central and peripheral levels, as well as between plasma and serum for some of these metabolites. Finally, changes in the kynurenine pathway are suggested to be a core component of AD. More studies are warranted to verify and consolidate our results.

A cross-sectional study of cognitive performance in bipolar disorder across the lifespan: the cog-BD project.

BACKGROUND: Neuroprogressive models of the trajectory of cognitive dysfunction in patients with bipolar disorder (BD) have been proposed. However, few studies have explored the relationships among clinical characteristics of BD, cognitive dysfunction, and aging. METHODS: We conducted a cross-sectional analysis in euthymic participants with the MATRICS Cognitive Consensus Battery, the Trail Making Test B, the Stroop Test, and the Wechsler Test of Adult Reading. Age- and gender-equated control participants without a mental disorder ['Healthy Controls' - HC)] were assessed similarly. We compared cognitive performance both globally and in seven domains in four groups: younger BD (age ⩽49 years; n = 70), older BD (age ⩾50 years; n = 48), younger HC (n = 153), and older HC (n = 44). We also compared the BD and HC groups using age as a continuous measure. We controlled for relevant covariates and applied a Bonferroni correction. RESULTS: Our results support both an early impairment ('early hit') model and an accelerated aging model: impairment in attention/vigilance, processing speed, and executive function/working memory were congruent with the accelerated aging hypothesis whereas impairment in verbal memory was congruent with an early impairment model. BD and HC participants exhibited similar age-related decline in reasoning/problem solving and visuospatial memory. There were no age- or diagnosis-related differences in social cognition. CONCLUSION: Our findings support that different cognitive domains are affected differently by BD and aging. Longitudinal studies are needed to explore trajectories of cognitive performance in BD across the lifespan.

The kynurenine pathway in Alzheimer's disease: a meta-analysis of central and peripheral levels

Objective: Changes in the kynurenine pathway are recognized in psychiatric disorders, but their role in Alzheimer's disease (AD) is less clear. We aimed to conduct a systematic review and meta-analysis to determine whether tryptophan and kynurenine pathway metabolites are altered in AD. Methods: We performed a systematic review and random-effects meta-analyses. Inclusion criteria were studies that compared AD and cognitively normal (CN) groups and assessed tryptophan or kynurenine pathway metabolites in cerebrospinal fluid or peripheral blood. Results: Twenty-two studies with a total of 1,356 participants (664 with AD and 692 CN individuals) were included. Tryptophan was decreased only in peripheral blood. The kynurenine-to-tryptophan ratio was only increased in peripheral blood of the AD group. 3-Hydroxykynurenine was decreased only in cerebrospinal fluid and showed higher variability in the CN group than the AD group. Kynurenic acid was increased in cerebrospinal fluid and decreased in peripheral blood. Finally, there were no changes in kynurenine and quinolinic acid between the groups. Conclusions: Our results suggested a shift toward the kynurenine pathway in both the brain and in the periphery, as well as a shift towards increased kynurenic acid production in the brain but decreased production in peripheral blood. In addition, our analysis indicated dissociation between the central and peripheral levels, as well as between plasma and serum for some of these metabolites. Finally, changes in the kynurenine pathway are suggested to be a core component of AD. More studies are warranted to verify and consolidate our results.

Insulin resistance in depression: A large meta-analysis of metabolic parameters and variation.

Increased insulin resistance is recognized in psychiatric disorders, such as schizophrenia and bipolar disorder, but its occurrence in depression is less clear. Our aims were to verify if insulin resistance is altered in depression, to test the metabolic subgroup hypothesis of depression and if there are changes with antidepressants. Inclusion criteria were studies including adult subjects with depression and either a control group or follow-up after treatment with antidepressants, and assessing fasting insulin or glucose levels or the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) index. Seventy studies with 240,704 participants were included. Both insulin levels and the HOMA-IR index were increased in acute depression. Neither insulin nor the HOMA-IR index were altered during remission. Insulin was increased in atypical, but not typical depression. There was higher variation in insulin in individuals with depression than in controls. Insulin resistance did not change with antidepressant treatment. Insulin resistance is increased in depression during acute episodes. Heterogeneity was high in most of the analyses. Laboratory assessment of insulin resistance might have clinical utility in people with depression for diagnosis of the metabolic subtype and treatment selection, following precision psychiatry standards.

Charting the proteome landscape in major psychiatric disorders: From biomarkers to biological pathways towards drug discovery.

Schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are major mental disorders that affect a significant proportion of the global population. Advancing our knowledge of the pathophysiology of these disorders and identifying biomarkers are urgent needs for developing objective diagnostic tests and new therapeutics. In this study, we performed a systematic review and then extracted, curated, and analyzed proteomics data from published studies, aiming to assess the proteome in peripheral blood of individuals with SZ, BD, or MDD. Then, we performed pathway and network analyses to illuminate the biological themes concatenated by the differentially expressed proteins by systematically interrogating the literature to uncover biological pathways with more robust biological meaning. We identified 486 differentially expressed proteins from 51 studies across the three disorders with 9,423 participants. The great majority of pathways were common to SZ, BD, and MDD. They were related to the immune system, including signaling by interleukins, Toll-like receptor signaling pathway, and complement cascade, and to signal transduction, notably MAPK1/MAPK3 signaling, PI3K-Akt Signaling Pathway, Focal Adhesion-PI3K-Akt-mTOR-signaling, rhodopsin-like receptors, GPCR signaling, and the JAK-STAT signaling pathway. Other shared pathways included advanced glycosylation end-product receptor signaling, Regulation of Insulin-like Growth Factor, cholesterol metabolism, and IL-17 signaling pathway. Pathways shared between SZ and BD were integrin cell-surface interactions, GRB2:SOS provides linkage to MAPK signaling for integrins, and syndecan interactions. Shared between BD and MDD were the NRF2 pathway and signaling by EGFR pathways. Our findings advance our understanding of the protein variations and associations with these disorders, which are useful for accelerating biomarker development and drug discovery.

Identifying candidate genes and drug targets for Alzheimer's disease by an integrative network approach using genetic and brain region-specific proteomic data.

Genome-wide association studies (GWAS) have identified more than 75 genetic variants associated with Alzheimer's disease (ad). However, how these variants function and impact protein expression in brain regions remain elusive. Large-scale proteomic datasets of ad postmortem brain tissues have become available recently. In this study, we used these datasets to investigate brain region-specific molecular pathways underlying ad pathogenesis and explore their potential drug targets. We applied our new network-based tool, Edge-Weighted Dense Module Search of GWAS (EW_dmGWAS), to integrate ad GWAS statistics of 472 868 individuals with proteomic profiles from two brain regions from two large-scale ad cohorts [parahippocampal gyrus (PHG), sample size n = 190; dorsolateral prefrontal cortex (DLPFC), n = 192]. The resulting network modules were evaluated using a scale-free network index, followed by a cross-region consistency evaluation. Our EW_dmGWAS analyses prioritized 52 top module genes (TMGs) specific in PHG and 58 TMGs in DLPFC, of which four genes (CLU, PICALM, PRRC2A and NDUFS3) overlapped. Those four genes were significantly associated with ad (GWAS gene-level false discovery rate < 0.05). To explore the impact of these genetic components on TMGs, we further examined their differentially co-expressed genes at the proteomic level and compared them with investigational drug targets. We pinpointed three potential drug target genes, APP, SNCA and VCAM1, specifically in PHG. Gene set enrichment analyses of TMGs in PHG and DLPFC revealed region-specific biological processes, tissue-cell type signatures and enriched drug signatures, suggesting potential region-specific drug repurposing targets for ad.

Treatment-resistant bipolar depression: concepts and challenges for novel interventions

Treatment-resistant bipolar depression (TRBD) has been reported in about one-quarter of patients with bipolar disorders, and few interventions have shown clear and established effectiveness. We conducted a narrative review of the published medical literature to identify papers discussing treatment-resistant depression concepts and novel interventions for bipolar depression that focus on TRBD. We searched for potentially relevant English-language articles published in the last decade. Selected articles (based on the title and abstract) were retrieved for a more detailed evaluation. A number of promising new interventions, both pharmacological and non-pharmacological, are being investigated for TRBD treatment, including ketamine, lurasidone, D-cycloserine, pioglitazone, N-acetylcysteine, angiotensin-converting enzyme inhibitors, angiotensin II type 1 receptor blockers, cyclooxygenase 2 inhibitors, magnetic seizure therapy, intermittent theta-burst stimulation, deep transcranial magnetic stimulation, vagus nerve stimulation therapy, and deep brain stimulation. Although there is no consensus about the concept of TRBD, better clarification of the neurobiology associated with treatment non-response could help identify novel strategies. More research is warranted, mainly focusing on personalizing current treatments to optimize response and remission rates.