PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data.

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO, http://pcaontology.net/ and http://pcaontology.net/mobile/.

Re-analysis of whole genome sequencing ends a diagnostic odyssey: Case report of an RNU4-2 related neurodevelopmental disorder.

Despite increasing knowledge of disease-causing genes in human genetics, approximately half of the individuals affected by neurodevelopmental disorders remain genetically undiagnosed. Part of this missing heritability might be caused by genetic variants outside of protein-coding genes, which are not routinely diagnostically investigated. A recent preprint identified de novo variants in the non-coding spliceosomal snRNA gene RNU4-2 as a cause of a frequent novel syndromic neurodevelopmental disorder. Here we mined 164 whole genome sequencing (WGS) trios from individuals with neurodevelopmental or multiple congenital anomaly disorders that received diagnostic genomic investigations at our clinic. We identify a recurrent de novo RNU4-2 variant (NR_003137.2(RNU4-2):n.64_65insT) in a 5-year-old girl with severe global developmental delay, hypotonia, microcephaly, and seizures that likely explains her phenotype, given that extensive previous genetic investigations failed to identify an alternative cause. We present detailed phenotyping of the individual obtained during a 5-year follow-up. This includes photographs showing recognizable facial features for this novel disorder, which might allow prioritizing other currently unexplained affected individuals sharing similar facial features for targeted investigations of RNU4-2. This case illustrates the power of re-analysis to solve previously unexplained cases even when a diagnostic genome remains negative.

Exploring intra-diagnosis heterogeneity and inter-diagnosis commonality in genetic architectures of bipolar disorders: association of polygenic risks of major psychiatric illnesses and lifetime phenotype dimensions.

BACKGROUND: Bipolar disorder (BD) shows heterogeneous illness presentation both cross-sectionally and longitudinally. This phenotypic heterogeneity might reflect underlying genetic heterogeneity. At the same time, overlapping characteristics between BD and other psychiatric illnesses are observed at clinical and biomarker levels, which implies a shared biological mechanism between them. Incorporating these two issues in a single study design, this study investigated whether phenotypically heterogeneous subtypes of BD have a distinct polygenic basis shared with other psychiatric illnesses. METHODS: Six lifetime phenotype dimensions of BD identified in our previous study were used as target phenotypes. Associations between these phenotype dimensions and polygenic risk scores (PRSs) of major psychiatric illnesses from East Asian (EA) and other available populations were analyzed. RESULTS: Each phenotype dimension showed a different association pattern with PRSs of mental illnesses. PRS for EA schizophrenia showed a significant negative association with the cyclicity dimension (p = 0.044) but a significant positive association with the psychotic/irritable mania dimension (p = 0.001). PRS of EA major depressive disorder demonstrated a significant negative association with the elation dimension (p = 0.003) but a significant positive association with the comorbidity dimension (p = 0.028). CONCLUSION: This study demonstrates that well-defined phenotype dimensions of lifetime-basis in BD have distinct genetic risks shared with other major mental illnesses. This finding supports genetic heterogeneity in BD and suggests a pleiotropy among BD subtypes and other psychiatric disorders beyond BD. Further genomic analyses adopting deep phenotyping across mental illnesses in ancestrally diverse populations are warranted to clarify intra-diagnosis heterogeneity and inter-diagnoses commonality issues in psychiatry.

First copy number variant in trans with single nucleotide variant in CCN6 causing progressive pseudorheumatoid dysplasia revealed by genome sequencing and deep phenotyping in monozygotic twins.

Biallelic pathogenic variants in CCN6 cause progressive pseudorheumatoid dysplasia (PPD), a rare skeletal dysplasia. The predominant features include noninflammatory progressive joint stiffness and enlargement, which are not unique to this condition. Nearly 100% of the reported variants are single nucleotide variants or small indels, and missing of a second variant has been reported. Genome sequencing (GS) covers various types of variants and deep phenotyping (DP) provides detailed and precise information facilitating genetic data interpretation. The combination of GS and DP improves diagnostic yield, especially in rare and undiagnosed diseases. We identified a novel compound heterozygote involving a disease-causing copy number variant (g.112057664_112064205del) in trans with a single nucleotide variant (c.624dup(p.Cys209MetfsTer21)) in CCN6 in a pair of monozygotic twins, through the methods of GS and DP. The twins had received three nondiagnostic results before. The g.112057664_112064205del variant was missed by all the tests, and the recorded phenotypes were inaccurate or even misleading. The twins were diagnosed with PPD, ending a 13-year diagnostic odyssey. There may be other patients with PPD experiencing underdiagnosis and misdiagnosis due to inadequate genetic testing or phenotyping methods. This case highlights the critical role of GS and DP in facilitating an accurate and timely diagnosis.

Analysis of acute COVID-19 including chronic morbidity: protocol for the deep phenotyping National Pandemic Cohort Network in Germany (NAPKON-HAP).

BACKGROUND: The severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) pandemic causes a high burden of acute and long-term morbidity and mortality worldwide despite global efforts in containment, prophylaxis, and therapy. With unprecedented speed, the global scientific community has generated pivotal insights into the pathogen and the host response evoked by the infection. However, deeper characterization of the pathophysiology and pathology remains a high priority to reduce morbidity and mortality of coronavirus disease 2019 (COVID-19). METHODS: NAPKON-HAP is a multi-centered prospective observational study with a long-term follow-up phase of up to 36 months post-SARS-CoV-2 infection. It constitutes a central platform for harmonized data and biospecimen for interdisciplinary characterization of acute SARS-CoV-2 infection and long-term outcomes of diverging disease severities of hospitalized patients. RESULTS: Primary outcome measures include clinical scores and quality of life assessment captured during hospitalization and at outpatient follow-up visits to assess acute and chronic morbidity. Secondary measures include results of biomolecular and immunological investigations and assessment of organ-specific involvement during and post-COVID-19 infection. NAPKON-HAP constitutes a national platform to provide accessibility and usability of the comprehensive data and biospecimen collection to global research. CONCLUSION: NAPKON-HAP establishes a platform with standardized high-resolution data and biospecimen collection of hospitalized COVID-19 patients of different disease severities in Germany. With this study, we will add significant scientific insights and provide high-quality data to aid researchers to investigate COVID-19 pathophysiology, pathology, and chronic morbidity.

Deep neurological phenotyping in oculo-dento-digital syndrome.

OBJECTIVES: Oculodentodigital dysplasia (ODDD) is a rare autosomal dominant congenital malformation syndrome characterized by high penetrance and great phenotypic heterogeneity. Neurological manifestations are thought to occur in about one third of cases, but systematic studies are not available. We performed deep neurological phenotyping of 10 patients in one ODDD pedigree. METHODS: Retrospective case series. We analyzed in depth the neurological phenotype of a three-generation family segregating the heterozygous c.416 T > C, p.(Ile139Thr) in GJA1. Clinical and neuroradiological features were retrospectively evaluated. Brain MRI and visual evoked potentials were performed in 8 and 6 cases, respectively. RESULTS: Central nervous system manifestations occurred in 5 patients, the most common being isolated ataxia either in isolation or combined with spasticity. Furthermore, sphincteric disturbances (neurogenic bladder and fecal incontinence) were recognized as the first manifestation in most of the patients. Subclinical electrophysiological alteration of the optic pathway occurred in all the examined patients. Neuroimaging was significant for supratentorial hypomyelination pattern and hyperintense superior cerebellar peduncle in all examined patients. CONCLUSION: The neurological involvement in ODDD carriers is often missed but peculiar clinical and radiological patterns can be recognized. Deep neurological phenotyping is needed to help untangle ODDD syndrome complexity and find genotype-phenotype correlations.

Association of deep phenotyping with diagnostic yield of prenatal exome sequencing for fetal brain abnormalities.

PURPOSE: To evaluate whether deep prenatal phenotyping of fetal brain abnormalities (FBAs) increases diagnostic yield of trio-exome sequencing (ES) compared with standard phenotyping. METHODS: Retrospective exploratory analysis of a multicenter prenatal ES study. Participants were eligible if an FBA was diagnosed and subsequently found to have a normal microarray. Deep phenotyping was defined as phenotype based on targeted ultrasound plus prenatal/postnatal magnetic resonance imaging, autopsy, and/or known phenotypes of other affected family members. Standard phenotyping was based on targeted ultrasound alone. FBAs were categorized by major brain findings on prenatal ultrasound. Cases with positive ES results were compared with those that have negative results by available phenotyping, as well as diagnosed FBAs. RESULTS: A total of 76 trios with FBAs were identified, of which 25 (33%) cases had positive ES results and 51 (67%) had negative results. Individual modalities of deep phenotyping were not associated with diagnostic ES results. The most common FBAs identified were posterior fossa anomalies and midline defects. Neural tube defects were significantly associated with receipt of a negative ES result (0% vs 22%, P = .01). CONCLUSION: Deep phenotyping was not associated with increased diagnostic yield of ES for FBA in this small cohort. Neural tube defects were associated with negative ES results.

Deep psychophysiological phenotyping of adolescents and adults with 22q11.2 deletion syndrome: a multilevel approach to defining core disease processes.

BACKGROUND: 22q11.2 deletion syndrome (22q11.2DS) is the most common chromosomal interstitial-deletion disorder, occurring in approximately 1 in 2000 to 6000 live births. Affected individuals exhibit variable clinical phenotypes that can include velopharyngeal anomalies, heart defects, T-cell-related immune deficits, dysmorphic facial features, neurodevelopmental disorders, including autism, early cognitive decline, schizophrenia, and other psychiatric disorders. Developing comprehensive treatments for 22q11.2DS requires an understanding of both the psychophysiological and neural mechanisms driving clinical outcomes. Our project probes the core psychophysiological abnormalities of 22q11.2DS in parallel with molecular studies of stem cell-derived neurons to unravel the basic mechanisms and pathophysiology of 22q11.2-related psychiatric disorders, with a primary focus on psychotic disorders. Our study is guided by the central hypothesis that abnormal neural processing associates with psychophysiological processing and underlies clinical diagnosis and symptomatology. Here, we present the scientific background and justification for our study, sharing details of our study design and human data collection protocol. METHODS: Our study is recruiting individuals with 22q11.2DS and healthy comparison subjects between the ages of 16 and 60 years. We are employing an extensive psychophysiological assessment battery (e.g., EEG, evoked potential measures, and acoustic startle) to assess fundamental sensory detection, attention, and reactivity. To complement these unbiased measures of cognitive processing, we will develop stem-cell derived neurons and examine neuronal phenotypes relevant to neurotransmission. Clinical characterization of our 22q11.2DS and control participants relies on diagnostic and research domain criteria assessments, including standard Axis-I diagnostic and neurocognitive measures, following from the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) and the North American Prodrome Longitudinal Study (NAPLS) batteries. We are also collecting measures of autism spectrum (ASD) and attention deficit/hyperactivity disorder (ADHD)-related symptoms. DISCUSSION: Studying 22q11.2DS in adolescence and adulthood via deep phenotyping across multiple clinical and biological domains may significantly increase our knowledge of its core disease processes. Our manuscript describes our ongoing study's protocol in detail. These paradigms could be adapted by clinical researchers studying 22q11.2DS, other CNV/single gene disorders, or idiopathic psychiatric syndromes, as well as by basic researchers who plan to incorporate biobehavioral outcome measures into their studies of 22q11.2DS.

Precision medicine integrating whole-genome sequencing, comprehensive metabolomics, and advanced imaging.

Genome sequencing has established clinical utility for rare disease diagnosis. While increasing numbers of individuals have undergone elective genome sequencing, a comprehensive study surveying genome-wide disease-associated genes in adults with deep phenotyping has not been reported. Here we report the results of a 3-y precision medicine study with a goal to integrate whole-genome sequencing with deep phenotyping. A cohort of 1,190 adult participants (402 female [33.8%]; mean age, 54 y [range 20 to 89+]; 70.6% European) had whole-genome sequencing, and were deeply phenotyped using metabolomics, advanced imaging, and clinical laboratory tests in addition to family/medical history. Of 1,190 adults, 206 (17.3%) had at least 1 genetic variant with pathogenic (P) or likely pathogenic (LP) assessment that suggests a predisposition of genetic risk. A multidisciplinary clinical team reviewed all reportable findings for the assessment of genotype and phenotype associations, and 137 (11.5%) had genotype and phenotype associations. A high percentage of genotype and phenotype associations (>75%) was observed for dyslipidemia (n = 24), cardiomyopathy, arrhythmia, and other cardiac diseases (n = 42), and diabetes and endocrine diseases (n = 17). A lack of genotype and phenotype associations, a potential burden for patient care, was observed in 69 (5.8%) individuals with P/LP variants. Genomics and metabolomics associations identified 61 (5.1%) heterozygotes with phenotype manifestations affecting serum metabolite levels in amino acid, lipid and cofactor, and vitamin pathways. Our descriptive analysis provides results on the integration of whole-genome sequencing and deep phenotyping for clinical assessments in adults.

Genomics4RD: An integrated platform to share Canadian deep-phenotype and multiomic data for international rare disease gene discovery.

Despite recent progress in the understanding of the genetic etiologies of rare diseases (RDs), a significant number remain intractable to diagnostic and discovery efforts. Broad data collection and sharing of information among RD researchers is therefore critical. In 2018, the Care4Rare Canada Consortium launched the project C4R-SOLVE, a subaim of which was to collect, harmonize, and share both retrospective and prospective Canadian clinical and multiomic data. Here, we introduce Genomics4RD, an integrated web-accessible platform to share Canadian phenotypic and multiomic data between researchers, both within Canada and internationally, for the purpose of discovering the mechanisms that cause RDs. Genomics4RD has been designed to standardize data collection and processing, and to help users systematically collect, prioritize, and visualize participant information. Data storage, authorization, and access procedures have been developed in collaboration with policy experts and stakeholders to ensure the trusted and secure access of data by external researchers. The breadth and standardization of data offered by Genomics4RD allows researchers to compare candidate disease genes and variants between participants (i.e., matchmaking) for discovery purposes, while facilitating the development of computational approaches for multiomic data analyses and enabling clinical translation efforts for new genetic technologies in the future.

Mutations in PIGU Impair the Function of the GPI Transamidase Complex, Causing Severe Intellectual Disability, Epilepsy, and Brain Anomalies.

The glycosylphosphatidylinositol (GPI) anchor links over 150 proteins to the cell surface and is present on every cell type. Many of these proteins play crucial roles in neuronal development and function. Mutations in 18 of the 29 genes implicated in the biosynthesis of the GPI anchor have been identified as the cause of GPI biosynthesis deficiencies (GPIBDs) in humans. GPIBDs are associated with intellectual disability and seizures as their cardinal features. An essential component of the GPI transamidase complex is PIGU, along with PIGK, PIGS, PIGT, and GPAA1, all of which link GPI-anchored proteins (GPI-APs) onto the GPI anchor in the endoplasmic reticulum (ER). Here, we report two homozygous missense mutations (c.209T>A [p.Ile70Lys] and c.1149C>A [p.Asn383Lys]) in five individuals from three unrelated families. All individuals presented with global developmental delay, severe-to-profound intellectual disability, muscular hypotonia, seizures, brain anomalies, scoliosis, and mild facial dysmorphism. Using multicolor flow cytometry, we determined a characteristic profile for GPI transamidase deficiency. On granulocytes this profile consisted of reduced cell-surface expression of fluorescein-labeled proaerolysin (FLAER), CD16, and CD24, but not of CD55 and CD59; additionally, B cells showed an increased expression of free GPI anchors determined by T5 antibody. Moreover, computer-assisted facial analysis of different GPIBDs revealed a characteristic facial gestalt shared among individuals with mutations in PIGU and GPAA1. Our findings improve our understanding of the role of the GPI transamidase complex in the development of nervous and skeletal systems and expand the clinical spectrum of disorders belonging to the group of inherited GPI-anchor deficiencies.

Measuring single-cell protein secretion in immunology: Technologies, advances, and applications.

The dynamics, nature, strength, and ultimately protective capabilities of an active immune response are determined by the extracellular constitution and concentration of various soluble factors. Generated effector cells secrete such mediators, including antibodies, chemo- and cytokines to achieve functionality. These secreted factors organize the individual immune cells into functional tissues, initiate, orchestrate, and regulate the immune response. Therefore, a single-cell resolved analysis of protein secretion is a valuable tool for studying the heterogeneity and functionality of immune cells. This review aims to provide a comparative overview of various methods to characterize immune reactions by measuring single-cell protein secretion. Spot-based and cytometry-based assays, such as ELISpot and flow cytometry, respectively, are well-established methods applied in basic research and clinical settings. Emerging novel technologies, such as microfluidic platforms, offer new ways to measure and exploit protein secretion in immune reactions. Further technological advances will allow the deciphering of protein secretion in immunological responses with unprecedented detail, linking secretion to functionality. Here, we summarize the development and recent advances of tools that allow the analysis of protein secretion at the single-cell level, and discuss and contrast their applications within immunology.

TTC5 syndrome: Clinical and molecular spectrum of a severe and recognizable condition.

Biallelic mutations in the TTC5 gene have been associated with autosomal recessive intellectual disability (ARID) and subsequently with an ID syndrome including severe speech impairment, cerebral atrophy, and hypotonia as clinical cornerstones. A TTC5 role in IDs has been proposed based on the physical interaction of TTC5 with p300, and possibly reducing p300 co-activator complex activity, similarly to what was observed in Menke-Hennekam 1 and 2 patients (MKHK1 and 2) carrying, respectively, mutations in exon 30 and 31 of CREBBP and EP300, which code for the TTC5-binding region. Recently, TTC5-related brain malformation has been linked to tubulinopathies due to the function of TTC5 in tubulins' dynamics. We reported seven new patients with novel or recurrent TTC5 variants. The deep characterization of the molecular and phenotypic spectrum confirmed TTC5-related disorder as a recognizable, very severe neurodevelopmental syndrome. In addition, other relevant clinical aspects, including a severe pre- and postnatal growth retardation, cryptorchidism, and epilepsy, have emerged from the reversal phenotype approach and the review of already published TTC5 cases. Microcephaly and facial dysmorphism resulted in being less variable than that documented before. The TTC5 clinical features have been compared with MKHK1 published cases in the hypothesis that clinical overlap in some characteristics of the two conditions was related to the common p300 molecular pathway.

MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record.

Electronic Health Records (EHRs) contain rich clinical data collected at the point of the care, and their increasing adoption offers exciting opportunities for clinical informatics, disease risk prediction, and personalized treatment recommendation. However, effective use of EHR data for research and clinical decision support is often hampered by a lack of reliable disease labels. To compile gold-standard labels, researchers often rely on clinical experts to develop rule-based phenotyping algorithms from billing codes and other surrogate features. This process is tedious and error-prone due to recall and observer biases in how codes and measures are selected, and some phenotypes are incompletely captured by a handful of surrogate features. To address this challenge, we present a novel automatic phenotyping model called MixEHR-Guided (MixEHR-G), a multimodal hierarchical Bayesian topic model that efficiently models the EHR generative process by identifying latent phenotype structure in the data. Unlike existing topic modeling algorithms wherein the inferred topics are not identifiable, MixEHR-G uses prior information from informative surrogate features to align topics with known phenotypes. We applied MixEHR-G to an openly-available EHR dataset of 38,597 intensive care patients (MIMIC-III) in Boston, USA and to administrative claims data for a population-based cohort (PopHR) of 1.3 million people in Quebec, Canada. Qualitatively, we demonstrate that MixEHR-G learns interpretable phenotypes and yields meaningful insights about phenotype similarities, comorbidities, and epidemiological associations. Quantitatively, MixEHR-G outperforms existing unsupervised phenotyping methods on a phenotype label annotation task, and it can accurately estimate relative phenotype prevalence functions without gold-standard phenotype information. Altogether, MixEHR-G is an important step towards building an interpretable and automated phenotyping system using EHR data.

Deep Phenotyping of Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives With a Sequence Motif Discovery Tool: Algorithm Development and Validation.

BACKGROUND: Phenotype information in electronic health records (EHRs) is mainly recorded in unstructured free text, which cannot be directly used for clinical research. EHR-based deep-phenotyping methods can structure phenotype information in EHRs with high fidelity, making it the focus of medical informatics. However, developing a deep-phenotyping method for non-English EHRs (ie, Chinese EHRs) is challenging. Although numerous EHR resources exist in China, fine-grained annotation data that are suitable for developing deep-phenotyping methods are limited. It is challenging to develop a deep-phenotyping method for Chinese EHRs in such a low-resource scenario. OBJECTIVE: In this study, we aimed to develop a deep-phenotyping method with good generalization ability for Chinese EHRs based on limited fine-grained annotation data. METHODS: The core of the methodology was to identify linguistic patterns of phenotype descriptions in Chinese EHRs with a sequence motif discovery tool and perform deep phenotyping of Chinese EHRs by recognizing linguistic patterns in free text. Specifically, 1000 Chinese EHRs were manually annotated based on a fine-grained information model, PhenoSSU (Semantic Structured Unit of Phenotypes). The annotation data set was randomly divided into a training set (n=700, 70%) and a testing set (n=300, 30%). The process for mining linguistic patterns was divided into three steps. First, free text in the training set was encoded as single-letter sequences (P: phenotype, A: attribute). Second, a biological sequence analysis tool-MEME (Multiple Expectation Maximums for Motif Elicitation)-was used to identify motifs in the single-letter sequences. Finally, the identified motifs were reduced to a series of regular expressions representing linguistic patterns of PhenoSSU instances in Chinese EHRs. Based on the discovered linguistic patterns, we developed a deep-phenotyping method for Chinese EHRs, including a deep learning-based method for named entity recognition and a pattern recognition-based method for attribute prediction. RESULTS: In total, 51 sequence motifs with statistical significance were mined from 700 Chinese EHRs in the training set and were combined into six regular expressions. It was found that these six regular expressions could be learned from a mean of 134 (SD 9.7) annotated EHRs in the training set. The deep-phenotyping algorithm for Chinese EHRs could recognize PhenoSSU instances with an overall accuracy of 0.844 on the test set. For the subtask of entity recognition, the algorithm achieved an F1 score of 0.898 with the Bidirectional Encoder Representations from Transformers-bidirectional long short-term memory and conditional random field model; for the subtask of attribute prediction, the algorithm achieved a weighted accuracy of 0.940 with the linguistic pattern-based method. CONCLUSIONS: We developed a simple but effective strategy to perform deep phenotyping of Chinese EHRs with limited fine-grained annotation data. Our work will promote the second use of Chinese EHRs and give inspiration to other non-English-speaking countries.

Incidental Findings from Deep Phenotyping Research in Psychiatry: Legal and Ethical Considerations.

Substantial advancement in the diagnosis and treatment of psychiatric disorders may come from assembling diverse data streams from clinical notes, neuroimaging, genetics, and real-time digital footprints from smartphones and wearable devices. This is called "deep phenotyping" and often involves machine learning. We argue that incidental findings arising in deep phenotyping research have certain special, morally and legally salient features: They are specific, actionable, numerous, and probabilistic. We consider ethical and legal implications of these features and propose a practical ethics strategy for managing them.

Deep phenotyping of synovial molecular signatures by integrative systems analysis in rheumatoid arthritis.

OBJECTIVE: RA encompasses a complex, heterogeneous and dynamic group of diseases arising from molecular and cellular perturbations of synovial tissues. The aim of this study was to decipher this complexity using an integrative systems approach and provide novel insights for designing stratified treatments. METHODS: An RNA sequencing dataset of synovial tissues from 152 RA patients and 28 normal controls was imported and subjected to filtration of differentially expressed genes, functional enrichment and network analysis, non-negative matrix factorization, and key driver analysis. A naïve Bayes classifier was applied to the independent datasets to investigate the factors associated with treatment outcome. RESULTS: A matrix of 1241 upregulated differentially expressed genes from RA samples was classified into three subtypes (C1-C3) with distinct molecular and cellular signatures. C3 with prominent immune cells and proinflammatory signatures had a stronger association with the presence of ACPA and showed a better therapeutic response than C1 and C2, which were enriched with neutrophil and fibroblast signatures, respectively. C2 was more occupied by synovial fibroblasts of destructive phenotype and carried highly expressed key effector molecules of invasion and osteoclastogenesis. CXCR2, JAK3, FYN and LYN were identified as key driver genes in C1 and C3. HDAC, JUN, NFKB1, TNF and TP53 were key regulators modulating fibroblast aggressiveness in C2. CONCLUSIONS: Deep phenotyping of synovial heterogeneity captured comprehensive and discrete pathophysiological attributes of RA regarding clinical features and treatment response. This result could serve as a template for future studies to design stratified approaches for RA patients.

Patients with acephalic spermatozoa syndrome linked to novel TSGA10/PMFBP1 variants have favorable pregnancy outcomes from intracytoplasmic sperm injection.

Acephalic spermatozoa syndrome is a rare form of teratozoospermia characterized by headless spermatozoa. Previous studies have found that variants in SUN5, PMFBP1, TSGA10, BRDT, and SPATC1L are associated with this phenotype. Many researchers have suggested that variants in TSGA10 without a proximal centriole might influence early embryonic development. This retrospective cohort study included 12 infertile men with severe acephalic spermatozoa in China. We identified six heterozygous variants and four homozygous variants in TSGA10/PMFBP1 in seven patients by whole-exome sequencing (WES). Acephalic spermatozoa defects due to different genetic variations may affect only spermatozoa morphology but do not reduce the chances of fertilization, affect embryo quality at early stages or impair intracytoplasmic sperm injection (ICSI) outcomes. Patients with TSGA10/PMFBP1 variations were all expected to have good prognoses with ICSI.

Longitudinal determination of resilience in humans to identify mechanisms of resilience to modern-life stressors: the longitudinal resilience assessment (LORA) study.

Resilience is the maintenance and/or quick recovery of mental health during and after periods of adversity. It is conceptualized to result from a dynamic process of successful adaptation to stressors. Up to now, a large number of resilience factors have been proposed, but the mechanisms underlying resilience are not yet understood. To shed light on the complex and time-varying processes of resilience that lead to a positive long-term outcome in the face of adversity, the Longitudinal Resilience Assessment (LORA) study has been established. In this study, 1191 healthy participants are followed up at 3- and 18-month intervals over a course of 4.5 years at two study centers in Germany. Baseline and 18-month visits entail multimodal phenotyping, including the assessment of mental health status, sociodemographic and lifestyle variables, resilience factors, life history, neuropsychological assessments (of proposed resilience mechanisms), and biomaterials (blood for genetic and epigenetic, stool for microbiome, and hair for cortisol analysis). At 3-monthly online assessments, subjects are monitored for subsequent exposure to stressors as well as mental health measures, which allows for a quantitative assessment of stressor-dependent changes in mental health as the main outcome. Descriptive analyses of mental health, number of stressors including major life events, daily hassles, perceived stress, and the ability to recover from stress are here presented for the baseline sample. The LORA study is unique in its design and will pave the way for a better understanding of resilience mechanisms in humans and for further development of interventions to successfully prevent stress-related disorder.

Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists.

The 21st century marks the emergence of "big data" with a rapid increase in the availability of datasets with multiple measurements. In neuroscience, brain-imaging datasets are more commonly accompanied by dozens or hundreds of phenotypic subject descriptors on the behavioral, neural, and genomic level. The complexity of such "big data" repositories offer new opportunities and pose new challenges for systems neuroscience. Canonical correlation analysis (CCA) is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities. Importantly, CCA is well suited to describing relationships across multiple sets of data, such as in recently available big biomedical datasets. Our primer discusses the rationale, promises, and pitfalls of CCA.