Integrated plasma proteomics identifies tuberculosis-specific diagnostic biomarkers.

BACKGROUNDNovel biomarkers to identify infectious patients transmitting Mycobacterium tuberculosis are urgently needed to control the global tuberculosis (TB) pandemic. We hypothesized that proteins released into the plasma in active pulmonary TB are clinically useful biomarkers to distinguish TB cases from healthy individuals and patients with other respiratory infections.METHODSWe applied a highly sensitive non-depletion tandem mass spectrometry discovery approach to investigate plasma protein expression in pulmonary TB cases compared to healthy controls in South African and Peruvian cohorts. Bioinformatic analysis using linear modeling and network correlation analyses identified 118 differentially expressed proteins, significant through 3 complementary analytical pipelines. Candidate biomarkers were subsequently analyzed in 2 validation cohorts of differing ethnicity using antibody-based proximity extension assays.RESULTSTB-specific host biomarkers were confirmed. A 6-protein diagnostic panel, comprising FETUB, FCGR3B, LRG1, SELL, CD14, and ADA2, differentiated patients with pulmonary TB from healthy controls and patients with other respiratory infections with high sensitivity and specificity in both cohorts.CONCLUSIONThis biomarker panel exceeds the World Health Organization Target Product Profile specificity criteria for a triage test for TB. The new biomarkers have potential for further development as near-patient TB screening assays, thereby helping to close the case-detection gap that fuels the global pandemic.FUNDINGMedical Research Council (MRC) (MR/R001065/1, MR/S024220/1, MR/P023754/1, and MR/W025728/1); the MRC and the UK Foreign Commonwealth and Development Office; the UK National Institute for Health Research (NIHR); the Wellcome Trust (094000, 203135, and CC2112); Starter Grant for Clinical Lecturers (Academy of Medical Sciences UK); the British Infection Association; the Program for Advanced Research Capacities for AIDS in Peru at Universidad Peruana Cayetano Heredia (D43TW00976301) from the Fogarty International Center at the US NIH; the UK Technology Strategy Board/Innovate UK (101556); the Francis Crick Institute, which receives funding from UKRI-MRC (CC2112); Cancer Research UK (CC2112); and the NIHR Biomedical Research Centre of Imperial College NHS.

Very early life microbiome and metabolome correlates with primary vaccination variability in children.

IMPORTANCE: We show that simultaneous study of stool and nasopharyngeal microbiome reveals divergent timing and patterns of maturation, suggesting that local mucosal factors may influence microbiome composition in the gut and respiratory system. Antibiotic exposure in early life as occurs commonly, may have an adverse effect on vaccine responsiveness. Abundance of gut and/or nasopharyngeal bacteria with the machinery to produce lipopolysaccharide-a toll-like receptor 4 agonist-may positively affect future vaccine protection, potentially by acting as a natural adjuvant. The increased levels of serum phenylpyruvic acid in infants with lower vaccine-induced antibody levels suggest an increased abundance of hydrogen peroxide, leading to more oxidative stress in low vaccine-responding infants.

Distinct subsets of neutrophils crosstalk with cytokines and metabolites in patients with sepsis.

Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Despite continued efforts to understand the pathophysiology of sepsis, no effective therapies are currently available. While singular components of the aberrant immune response have been investigated, comprehensive studies linking different data layers are lacking. Using an integrated systems immunology approach, we evaluated neutrophil phenotypes and concomitant changes in cytokines and metabolites in patients with sepsis. Our findings identify differentially expressed mature and immature neutrophil subsets in patients with sepsis. These subsets correlate with various proteins, metabolites, and lipids, including pentraxin-3, angiopoietin-2, and lysophosphatidylcholines, in patients with sepsis. These results enabled the construction of a statistical model based on weighted multi-omics linear regression analysis for sepsis biomarker identification. These findings could help inform early patient stratification and treatment options, and facilitate further mechanistic studies targeting the trifecta of surface marker expression, cytokines, and metabolites.

Vaccine Hyporesponse Induced by Individual Antibiotic Treatment in Mice and Non-Human Primates Is Diminished upon Recovery of the Gut Microbiome.

Emerging evidence demonstrates a connection between microbiome composition and suboptimal response to vaccines (vaccine hyporesponse). Harnessing the interaction between microbes and the immune system could provide novel therapeutic strategies for improving vaccine response. Currently we do not fully understand the mechanisms and dynamics by which the microbiome influences vaccine response. Using both mouse and non-human primate models, we report that short-term oral treatment with a single antibiotic (vancomycin) results in the disruption of the gut microbiome and this correlates with a decrease in systemic levels of antigen-specific IgG upon subsequent parenteral vaccination. We further show that recovery of microbial diversity before vaccination prevents antibiotic-induced vaccine hyporesponse, and that the antigen specific IgG response correlates with the recovery of microbiome diversity. RNA sequencing analysis of small intestine, spleen, whole blood, and secondary lymphoid organs from antibiotic treated mice revealed a dramatic impact on the immune system, and a muted inflammatory signature is correlated with loss of bacteria from Lachnospiraceae, Ruminococcaceae, and Clostridiaceae. These results suggest that microbially modulated immune pathways may be leveraged to promote vaccine response and will inform future vaccine design and development strategies.

The application potential of machine learning and genomics for understanding natural product diversity, chemistry, and therapeutic translatability.

Covering: up to the end of 2020. The machine learning field can be defined as the study and application of algorithms that perform classification and prediction tasks through pattern recognition instead of explicitly defined rules. Among other areas, machine learning has excelled in natural language processing. As such methods have excelled at understanding written languages (e.g. English), they are also being applied to biological problems to better understand the "genomic language". In this review we focus on recent advances in applying machine learning to natural products and genomics, and how those advances are improving our understanding of natural product biology, chemistry, and drug discovery. We discuss machine learning applications in genome mining (identifying biosynthetic signatures in genomic data), predictions of what structures will be created from those genomic signatures, and the types of activity we might expect from those molecules. We further explore the application of these approaches to data derived from complex microbiomes, with a focus on the human microbiome. We also review challenges in leveraging machine learning approaches in the field, and how the availability of other "omics" data layers provides value. Finally, we provide insights into the challenges associated with interpreting machine learning models and the underlying biology and promises of applying machine learning to natural product drug discovery. We believe that the application of machine learning methods to natural product research is poised to accelerate the identification of new molecular entities that may be used to treat a variety of disease indications.

Micro RNA Targets in HIV Latency: Insights into Novel Layers of Latency Control.

Despite the considerable progress that has been made in identifying cellular factors and pathways that contribute to establishment and maintenance of the latent HIV reservoir, it remains the major obstacle to eradicating this virus. Most recently, noncoding genes have been implicated in regulation of HIV expression. In this study, small RNA sequencing was used to profile expression of microRNAs (miRNAs) in a primary CD4+ T cell in vitro model of HIV latency. Previously, we have shown that protein-coding genes dysregulated in this model were enriched for the p53 signaling pathway, which was confirmed experimentally. We further found a link between p53 signaling and dysregulated long noncoding RNAs. In this study, we hypothesized that miRNAs may provide an additional level of regulation of the p53 signaling pathway during HIV latency. Twenty-six miRNAs were identified to be dysregulated in our latency model. A subset of these miRNAs was validated by real-time quantitative polymerase chain reaction. Predicted messenger RNA (mRNA) targets and cellular pathways enriched for mRNA targets were identified using several analytical methods. Our analyses showed that many protein-coding genes and pathways targeted by dysregulated miRNAs have relevance to regulation of HIV expression or establishment of HIV latency. The p53 signaling pathway was found among pathways that were targeted by dysregulated miRNAs at a greater level than expected by chance. This study provides a mechanistic insight into regulation of the p53 pathway through miRNAs that may contribute to the establishment of latency.

CTEN Induces Tumour Cell Invasion and Survival and Is Prognostic in Radiotherapy-Treated Head and Neck Cancer.

Head and neck squamous cell carcinoma (HNSCC) is a heterogenous disease treated with surgery and/or (chemo) radiotherapy, but up to 50% of patients with late-stage disease develop locoregional recurrence. Determining the mechanisms underpinning treatment resistance could identify new therapeutic targets and aid treatment selection. C-terminal tensin-like (CTEN) is a member of the tensin family, upregulated in several cancers, although its expression and function in HNSCC are unknown. We found that CTEN is commonly upregulated in HNSCC, particularly HPV-ve tumours. In vitro CTEN was upregulated in HPV-ve (n = 5) and HPV+ve (n = 2) HNSCC cell lines. Stable shRNA knockdown of CTEN in vivo significantly reduced tumour growth (SCC-25), and functional analyses in vitro showed that CTEN promoted tumour cell invasion, colony formation and growth in 3D-culture (SCC-25, Detroit 562). RNA sequencing of SCC-25 cells following CTEN siRNA knockdown identified 349 differentially expressed genes (logFC > 1, p < 0.05). Gene ontology analysis highlighted terms relating to cell locomotion and apoptosis, consistent with in vitro findings. A membrane-based antibody array confirmed that CTEN regulated multiple apoptosis-associated proteins, including HSP60 and cleaved caspase-3. Notably, in a mixed cohort of HPV+ve and HPV-ve HNSCC patients (n = 259), we found a significant, independent negative association of CTEN with prognosis, limited to those patients treated with (chemo)radiotherapy, not surgery, irrespective of human papillomavirus (HPV) status. These data show that CTEN is commonly upregulated in HNSCC and exerts several functional effects. Its potential role in modulating apoptotic response to therapy suggests utility as a predictive biomarker or radio-sensitising target.

Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis.

BACKGROUNDTuberculosis (TB) kills more people than any other infection, and new diagnostic tests to identify active cases are required. We aimed to discover and verify novel markers for TB in nondepleted plasma.METHODSWe applied an optimized quantitative proteomics discovery methodology based on multidimensional and orthogonal liquid chromatographic separation combined with high-resolution mass spectrometry to study nondepleted plasma of 11 patients with active TB compared with 10 healthy controls. Prioritized candidates were verified in independent UK (n = 118) and South African cohorts (n = 203).RESULTSWe generated the most comprehensive TB plasma proteome to date, profiling 5022 proteins spanning 11 orders-of-magnitude concentration range with diverse biochemical and molecular properties. We analyzed the predominantly low-molecular weight subproteome, identifying 46 proteins with significantly increased and 90 with decreased abundance (peptide FDR ≤ 1%, q ≤ 0.05). Verification was performed for novel candidate biomarkers (CFHR5, ILF2) in 2 independent cohorts. Receiver operating characteristics analyses using a 5-protein panel (CFHR5, LRG1, CRP, LBP, and SAA1) exhibited discriminatory power in distinguishing TB from other respiratory diseases (AUC = 0.81).CONCLUSIONWe report the most comprehensive TB plasma proteome to date, identifying novel markers with verification in 2 independent cohorts, leading to a 5-protein biosignature with potential to improve TB diagnosis. With further development, these biomarkers have potential as a diagnostic triage test.FUNDINGColciencias, Medical Research Council, Innovate UK, NIHR, Academy of Medical Sciences, Program for Advanced Research Capacities for AIDS, Wellcome Centre for Infectious Diseases Research.

Upregulation of epithelial metallothioneins by metal-rich ultrafine particulate matter from an underground railway.

Airborne particulate matter (PM) is a leading cause of mortality and morbidity. However, understanding of the range and mechanisms of effects of PM components is poor. PM generated in underground railways is rich in metals, especially iron. In the ultrafine (UFPM; <0.1 µm diameter) fraction, the combination of small size and metal enrichment poses an unknown health risk. This study aimed to analyse transcriptomic responses to underground UFPM in primary bronchial epithelial cells (PBECs), a key site of PM deposition. The oxidation state of iron in UFPM from an underground station was determined by X-ray absorption near edge structure (XANES) spectroscopy. Antioxidant response was assayed using a reporter cell line transfected with an antioxidant response element (ARE)-luciferase construct. Differentiated PBECs were exposed to UFPM for 6 h or 24 h for RNA-Seq and RT-qPCR analysis. XANES showed predominance of redox-active Fe3O4, with ROS generation confirmed by induction of ARE-luciferase expression. 6 h exposure of PBECs to UFPM identified 52 differentially expressed genes (DEGs), especially associated with epithelial maintenance, whereas 24 h exposure yielded 23 DEGs, particularly involved with redox homeostasis and metal binding. At both timepoints, there was upregulation of members of the metallothionein family, low molecular weight proteins with antioxidant activity whose main function is binding and homeostasis of zinc and copper ions, but not iron ions. This upregulation was partially inhibited by metal chelation or ROS scavenging. These data suggest differential regulation of responses to metal-rich UFPM depending on exposure period, and highlight novel pathways and markers of PM exposure, with the role of metallothioneins warranting further investigation.

Genomic programming of IRF4-expressing human Langerhans cells.

Langerhans cells (LC) can prime tolerogenic as well as immunogenic responses in skin, but the genomic states and transcription factors (TF) regulating these context-specific responses are unclear. Bulk and single-cell transcriptional profiling demonstrates that human migratory LCs are robustly programmed for MHC-I and MHC-II antigen presentation. Chromatin analysis reveals enrichment of ETS-IRF and AP1-IRF composite regulatory elements in antigen-presentation genes, coinciding with expression of the TFs, PU.1, IRF4 and BATF3 but not IRF8. Migration of LCs from the epidermis is accompanied by upregulation of IRF4, antigen processing components and co-stimulatory molecules. TNF stimulation augments LC cross-presentation while attenuating IRF4 expression. CRISPR-mediated editing reveals IRF4 to positively regulate the LC activation programme, but repress NF2EL2 and NF-kB pathway genes that promote responsiveness to oxidative stress and inflammatory cytokines. Thus, IRF4-dependent genomic programming of human migratory LCs appears to enable LC maturation while attenuating excessive inflammatory and immunogenic responses in the epidermis.

An evaluation of different classification algorithms for protein sequence-based reverse vaccinology prediction.

Previous work has shown that proteins that have the potential to be vaccine candidates can be predicted from features derived from their amino acid sequences. In this work, we make an empirical comparison across various machine learning classifiers on this sequence-based inference problem. Using systematic cross validation on a dataset of 200 known vaccine candidates and 200 negative examples, with a set of 525 features derived from the AA sequences and feature selection applied through a greedy backward elimination approach, we show that simple classification algorithms often perform as well as more complex support vector kernel machines. The work also includes a novel cross validation applied across bacterial species, i.e. the validation proteins all come from a specific species of bacterium not represented in the training set. We termed this type of validation Leave One Bacteria Out Validation (LOBOV).

Long non-coding RNAs and latent HIV - A search for novel targets for latency reversal.

The latent cellular reservoir of HIV is recognized as the major barrier to cure from HIV infection. Long non-coding RNAs (lncRNAs) are more tissue and cell type-specific than protein coding genes, and may represent targets of choice for HIV latency reversal. Using two in vitro primary T-cell models, we identified lncRNAs dysregulated in latency. PVT1 and RP11-347C18.3 were up-regulated in common between the two models, and RP11-539L10.2 was down-regulated. The major component of the latent HIV reservoir, memory CD4+ T-cells, had higher expression of these lncRNAs, compared to naïve T-cells. Guilt-by-association analysis demonstrated that lncRNAs dysregulated in latency were associated with several cellular pathways implicated in HIV latency establishment and maintenance: proteasome, spliceosome, p53 signaling, and mammalian target of rapamycin (MTOR). PVT1, RP11-347C18.3, and RP11-539L10.2 were down-regulated by latency reversing agents, suberoylanilide hydroxamic acid and Romidepsin, suggesting that modulation of lncRNAs is a possible secondary mechanism of action of these compounds. These results will facilitate prioritization of lncRNAs for evaluation as targets for HIV latency reversal. Importantly, our study provides insights into regulatory function of lncRNA during latent HIV infection.

A deep learning genome-mining strategy for biosynthetic gene cluster prediction.

Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers reduced false positive rates in BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing machine-learning tools. We supplemented this with random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable putative BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a major addition to in-silico BGC identification.

Immunological biomarkers as indicators for outcome after discontinuation of nucleos(t)ide analogue therapy in patients with HBeAg-negative chronic hepatitis B.

The optimal duration of treatment with nucleos(t)ide analogues (NAs) for patients with HBeAg-negative chronic hepatitis B (CHB) is unknown. The aim of this study was to identify an immune signature associated with off-treatment remission to NA therapy. We performed microarray analysis of peripheral blood mononuclear cell (PBMCs) from six patients with chronic hepatitis B who stopped NA therapy (three with off-treatment remission, three with relapse) and five patients with chronic HBV infection (previously termed 'inactive carriers') served as controls. Results were validated using qRT-PCR on a second group of 21 individuals (17 patients who stopped treatment and four controls). PBMCs from 38 patients on long-term NA treatment were analysed for potential to stop treatment. Microarray analysis indicated that patients with off-treatment remission segregated as a distinct out-group. Twenty-one genes were selected for subsequent validation. Ten of these were expressed at significantly lower levels in the patients with off-treatment remission compared to the patients with relapse and predicted remission with AUC of 0.78-0.92. IFNγ, IL-8, FASLG and CCL4 were the most significant by logistic regression. Twelve (31.6%) of 38 patients on long-term NA therapy had expression levels of all these four genes below cut-off values and hence were candidates for stopping treatment. Our data suggest that patients with HBeAg-negative CHB who remain in off-treatment remission 3 years after NA cessation have a distinct immune signature and that PBMC RNA levels of IFNγ, IL-8, FASLG and CCL4 may serve as potential biomarkers for stopping NA therapy.

HPV, tumour metabolism and novel target identification in head and neck squamous cell carcinoma.

BACKGROUND: Metabolic changes in tumour cells are used in clinical imaging and may provide potential therapeutic targets. Human papillomavirus (HPV) status is important in classifying head and neck cancers (HNSCC), identifying a distinct clinical phenotype; metabolic differences between these HNSCC subtypes remain poorly understood. METHODS: We used RNA sequencing to classify the metabolic expression profiles of HPV+ve and HPV-ve HNSCC, performed a meta-analysis on FDG-PET imaging characteristics and correlated results with in vitro extracellular flux analysis of HPV-ve and HPV+ve HNSCC cell lines. The monocarboxylic acid transporter-1 (MCT1) was identified as a potential metabolic target and tested in functional assays. RESULTS: Specific metabolic profiles were associated with HPV status, not limited to carbohydrate metabolism. There was dominance of all energy pathways in HPV-negative disease, with elevated expression of genes associated with glycolysis and oxidative phosphorylation. In vitro analysis confirmed comparative increased rates of oxidative phosphorylation and glycolysis in HPV-negative cell lines. PET SUV(max) scores however were unable to reliably differentiate between HPV-positive and HPV-negative tumours. MCT1 expression was significantly increased in HPV-negative tumours, and inhibition suppressed tumour cell invasion, colony formation and promoted radiosensitivity. CONCLUSION: HPV-positive and negative HNSCC have different metabolic profiles which may have potential therapeutic applications.

Patients with Chronic Obstructive Pulmonary Disease harbour a variation of Haemophilus species.

H. haemolyticus is often misidentified as NTHi due to their close phylogenetic relationship. Differentiating between the two is important for correct identification and appropriate treatment of infective organism and to ensure any role of H. haemolyticus in disease is not being overlooked. Speciation however is not completely reliable by culture and PCR methods due to the loss of haemolysis by H. haemolyticus and the heterogeneity of NTHi. Haemophilus isolates from COPD as part of the AERIS study (ClinicalTrials - NCT01360398) were speciated by analysing sequence data for the presence of molecular markers. Further investigation into the genomic relationship was carried out using average nucleotide identity and phylogeny of allelic and genome alignments. Only 6.3% were identified as H. haemolyticus. Multiple in silico methods were able to distinguish H. haemolyticus from NTHi. However, no single gene target was found to be 100% accurate. A group of omp2 negative NTHi were observed to be phylogenetically divergent from H. haemolyticus and remaining NTHi. The presence of an atypical group from a geographically and disease limited set of isolates supports the theory that the heterogeneity of NTHi may provide a genetic continuum between NTHi and H. haemolyticus.

Transcriptional Modulation of Human Endogenous Retroviruses in Primary CD4+ T Cells Following Vorinostat Treatment.

The greatest obstacle to a cure for HIV is the provirus that integrates into the genome of the infected cell and persists despite antiretroviral therapy. A "shock and kill" approach has been proposed as a strategy for an HIV cure whereby drugs and compounds referred to as latency-reversing agents (LRAs) are used to "shock" the silent provirus into active replication to permit "killing" by virus-induced pathology or immune recognition. The LRA most utilized to date in clinical trials has been the histone deacetylase (HDAC) inhibitor-vorinostat. Potentially, pathological off-target effects of vorinostat may result from the activation of human endogenous retroviruses (HERVs), which share common ancestry with exogenous retroviruses including HIV. To explore the effects of HDAC inhibition on HERV transcription, an unbiased pharmacogenomics approach (total RNA-Seq) was used to evaluate HERV expression following the exposure of primary CD4+ T cells to a high dose of vorinostat. Over 2,000 individual HERV elements were found to be significantly modulated by vorinostat, whereby elements belonging to the ERVL family (e.g., LTR16C and LTR33) were predominantly downregulated, in contrast to LTR12 elements of the HERV-9 family, which exhibited the greatest signal, with the upregulation of 140 distinct elements. The modulation of three different LTR12 elements by vorinostat was confirmed by droplet digital PCR along a dose-response curve. The monitoring of LTR12 expression during clinical trials with vorinostat may be indicated to assess the impact of this HERV on the human genome and host immunity.

The adhesins of non-typeable Haemophilus influenzae.

INTRODUCTION: Nontypeable Haemophilus influenzae (NTHi) is an opportunistic pathogen of the respiratory tract and the greatest contributor to invasive Haemophilus disease. Additionally, in children, NTHi is responsible for the majority of otitis media (OM) which can lead to chronic infection and hearing loss. In adults, NTHi infection in the lungs is responsible for the onset of acute exacerbations in chronic obstructive pulmonary disease (COPD). Unfortunately, there is currently no vaccine available to protect against NTHi infections. Areas covered: NTHi uses an arsenal of adhesins to colonise the respiratory epithelium. The adhesins also have secondary roles that aid in the virulence of NTHi, including mechanisms that avoid immune clearance, adjust pore size to avoid antimicrobial destruction, form micro-colonies and invoke phase variation for protein mediation. Bacterial adhesins can also be ideal antigens for subunit vaccine design due to surface exposure and immunogenic capabilities. Expert commentary: The host-pathogen interactions of the NTHi adhesins are not fully investigated. The relationship between adhesins and the extracellular matrix (ECM) play a part in the success of NTHi colonisation and virulence by immune evasion, migration and biofilm development. Further research into these immunogenic proteins would further our understanding and enable a basis for better combatting NTHi disease.

PTSD Blood Transcriptome Mega-Analysis: Shared Inflammatory Pathways across Biological Sex and Modes of Trauma.

Transcriptome-wide screens of peripheral blood during the onset and development of posttraumatic stress disorder (PTSD) indicate widespread immune dysregulation. However, little is known as to whether biological sex and the type of traumatic event influence shared or distinct biological pathways in PTSD. We performed a combined analysis of five independent PTSD blood transcriptome studies covering seven types of trauma in 229 PTSD and 311 comparison individuals to synthesize the extant data. Analyses by trauma type revealed a clear pattern of PTSD gene expression signatures distinguishing interpersonal (IP)-related traumas from combat-related traumas. Co-expression network analyses integrated all data and identified distinct gene expression perturbations across sex and modes of trauma in PTSD, including one wound-healing module downregulated in men exposed to combat traumas, one IL-12-mediated signaling module upregulated in men exposed to IP-related traumas, and two modules associated with lipid metabolism and mitogen-activated protein kinase activity upregulated in women exposed to IP-related traumas. Remarkably, a high degree of sharing of transcriptional dysregulation across sex and modes of trauma in PTSD was also observed converging on common signaling cascades, including cytokine, innate immune, and type I interferon pathways. Collectively, these findings provide a broad view of immune dysregulation in PTSD and demonstrate inflammatory pathways of molecular convergence and specificity, which may inform mechanisms and diagnostic biomarkers for the disorder.