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Mutations in MeCP2 result in a crippling neurological disease, but we lack a lucid picture of MeCP2′s molecular role. Individual transcriptomic studies yield inconsistent differentially expressed genes. To overcome these issues, we demonstrate a methodology to analyze all modern public data. We obtained relevant raw public transcriptomic data from GEO and ENA, then homogeneously processed it (QC, alignment to reference, differential expression analysis). We present a web portal to interactively access the mouse data, and we discovered a commonly perturbed core set of genes that transcends the limitations of any individual study. We then found functionally distinct, consistently up- and downregulated subsets within these genes and some bias to their location. We present this common core of genes as well as focused cores for up, down, cell fraction models, and some tissues. We observed enrichment for this mouse core in other species MeCP2 models and observed overlap with ASD models. By integrating and examining transcriptomic data at scale, we have uncovered the true picture of this dysregulation. The vast scale of these data enables us to analyze signal-to-noise, evaluate a molecular signature in an unbiased manner, and demonstrate a framework for future disease focused informatics work.

SummaryAging is a major risk factor for Alzheimer’s disease (AD), and cell-type vulnerability underlies its characteristic clinical manifestations. We have performed longitudinal, single-cell RNA-sequencing inDrosophilawith pan-neuronal expression of human tau, which forms AD neurofibrillary tangle pathology. Whereas tau- and aging-induced gene expression strongly overlap (93%), they differ in the affected cell types. In contrast to the broad impact of aging, tau-triggered changes are strongly polarized to excitatory neurons and glia. Further, tau can either activate or suppress innate immune gene expression signatures in a cell type-specific manner. Integration of cellular abundance and gene expression pinpoints Nuclear Factor Kappa B signaling as a potential marker for neuronal vulnerability. We also highlight the conservation of cell type-specific transcriptional patterns betweenDrosophilaand human postmortem brain tissue. Overall, our results create a resource for dissection of dynamic, age-dependent gene expression changes at cellular resolution in a genetically tractable model of tauopathy.

With the growing availability of Alzheimer’s disease (AD) transcriptomic data, several studies have nominated new therapeutic targets. However, a major challenge is accounting for latent (hidden) factors which affect the discovery of therapeutic targets. Using unsupervised machine learning, we identified a latent factor in brain tissue, and we validated the factor in AD and normal samples, across multiple studies, and different brain tissues. Moreover, significant metabolic differences were observed due to the latent factor. The latent factor was found to reflect cell-type heterogeneity in the brain and after adjusting for it, we were able to identify new biological pathways. The changes observed at both transcriptomic and metabolomic levels support the importance of identifying any latent factors before pursuing downstream analysis to accurately identify biomarkers.

Understanding the associations between genes is crucial to understanding the relationship between diseases. In order to learn these gene-gene associations and generate the huge gene network, more than 12,000 experiments have been analyzed. However, the correlation between genes is context-dependent, so two genes may not always be concordantly or discordantly co-dysregulated in all experiments, and the batch effect between experiments decreases the quality of the integrated data. We therefore developed a new co-perturbation model to identify reliable gene-gene correlations in the big integrated data, which significantly outperformed the widely used co-expression approach and can avoid Simpson’s paradox. Disease-related genes in our co-perturbation network are also more likely to be the hub genes, and the correlation between disease-related genes can be context dependent and non-linear. [We also validated biological findings between GeneA and B…]

BackgroundOpen science and collaboration are necessary to facilitate the advancement of Parkinson’s disease (PD) research. Hackathons are collaborative events that bring together people with different skill sets and backgrounds to generate resources and creative solutions to problems. These events can be used as training and networking opportunities.ObjectiveTo coordinate a virtual hackathon to develop novel PD research tools.Methods49 early career scientists from 12 countries collaborated in a virtual 3-day hackathon event in May 2021, during which they built tools and pipelines with a focus on PD. Resources were created with the goal of helping scientists accelerate their own research by having access to the necessary code and tools.ResultsEach team was allocated one of nine different projects, each with a different goal. These included developing post-genome-wide association studies (GWAS) analysis pipelines, downstream analysis of genetic variation pipelines, and various visualization tools.ConclusionHackathons are a valuable approach to inspire creative thinking, supplement training in data science, and foster collaborative scientific relationships, which are foundational practices for early career researchers. The resources generated can be used to accelerate research on the genetics of PD.

SummaryMutations in MeCP2 result in a crippling neurological disease, but we lack a lucid picture of MeCP2s molecular role. Focusing on individual transcriptomic studies yields inconsistent differentially expressed genes. We have aggregated and homogeneously processed modern public MeCP2 transcriptome data, which we present in a web portal. With this big data, we discovered a commonly perturbed core set of genes that transcends the limitations of any individual study. We then found distinct consistently up and downregulated subsets within these genes. We observe enrichment for this mouse core in other species MeCP2 models and see overlap between this core and ASD models. Analysis of signal to noise finds that many studies lack enough biological replicates. By integrating and examining transcriptomic data at scale, we have generated a valuable resource and insight on MeCP2 function.

Background and ObjectivesGenetic variants affect both Parkinson disease (PD) risk and manifestations. Although genetic information is of potential interest to patients and clinicians, genetic testing is rarely performed during routine PD clinical care. The goal of this study was to examine interest in comprehensive genetic testing among patients with PD and document reactions to possible findings from genome sequencing in 2 academic movement disorder clinics.MethodsIn 203 subjects with PD (age = 63 years, 67% male), genome sequencing was performed and filtered using a custom panel, including 49 genes associated with PD, parkinsonism, or related disorders, as well as a 90-variant PD genetic risk score. Based on the results, 231 patients (age = 67 years, 63% male) were surveyed on interest in genetic testing and responses to vignettes covering (1) familial risk of PD (LRRK2); (2) risk of PD dementia (GBA); (3) PD genetic risk score; and (4) secondary, medically actionable variants (BRCA1).ResultsGenome sequencing revealed a LRRK2 variant in 3% and a GBA risk variant in 10% of our clinical sample. The genetic risk score was normally distributed, identifying 41 subjects with a high risk of PD. Medically actionable findings were discovered in 2 subjects (1%). In our survey, the majority (82%) responded that they would share a LRRK2 variant with relatives. Most registered unchanged or increased interest in testing when confronted with a potential risk for dementia or medically actionable findings, and most (75%) expressed interest in learning their PD genetic risk score.DiscussionOur results highlight broad interest in comprehensive genetic testing among patients with PD and may facilitate integration of genome sequencing in clinical practice.

Background and ObjectivesGenetic variants impact both Parkinson’s disease (PD) risk and manifestations. While genetic information is of potential interest to patients and clinicians, genetic testing is rarely performed during routine PD clinical care. The goal of this study was to perform genome sequencing and examine patient interest in comprehensive genetic testing for PD in 2 academic movement disorder clinics.MethodsIn 208 subjects with PD (age=63 years, 67% male), genome sequencing was performed and filtered using a custom panel, including 49 genes associated with PD, parkinsonism, or related disorders, as well as a 90-variant PD genetic risk score. Separately, 231 patients (age=67 years, 63% male) were surveyed on interest in genetic testing at baseline and in response to vignettes covering (i) familial risk of PD (LRRK2); (ii) risk of PD dementia (GBA); (iii) PD genetic risk score; and (iv) secondary, medically-actionable variants (BRCA1).ResultsGenome sequencing revealed a LRRK2 variant in 3.4% and a GBA risk variant in 10.1% of our clinical sample. The genetic risk score was normally distributed, identifying 42 subjects with high risk of PD. Medically-actionable findings were discovered in 2 subjects (1%). In our survey, the majority (82%) responded they would share a LRRK2 variant with relatives. Most registered unchanged or increased interest in testing when confronted with potential risk for dementia or medically- actionable findings, and most (75%) expressed interest in learning their PD genetic risk score.DiscussionOur results highlight broad interest in comprehensive genetic testing among patients with PD and may facilitate integration of genome sequencing in clinical practice.

The CRISPR-Cas9 genome editing-based lineage tracing system is emerging as a powerful tool to track cell lineages at unprecedented scale and resolution. However, the complexity of CRISPR-Cas9 induced mutations has raised challenges in lineage reconstruction, which requires a unique computational analysis framework. Meanwhile, multiple distinctive CRISPR-based high-throughput lineage recorders have been developed over the years in which the data analysis is incompatible across platforms. To address these challenges, first, we present the TraceQC, a cross-platform open-source package for data processing and quality evaluation of CRISPR lineage tracing data. Second, by using the TraceQC package, we performed a comprehensive analysis across multiple CRISPR lineage recorders to uncover the speed and distribution of CRISPR-induced mutations. Together, this work provides a computational framework for the CRISPR lineage tracing system that should broadly benefit the design and application of this promising technology.

SUMMARY We present a consensus atlas of the human brain transcriptome in Alzheimer’s disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington’s disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies., Graphical Abstract, In Brief Wan et al. develop a consensus atlas of the human brain transcriptome in Alzheimer’s disease, based on 2,114 postmortem samples, and examine overlap with 251 expression signatures from mouse brain RNA sequencing experiments, including many neurodegenerative disease models.

OBJECTIVETo discover genetic determinants of Parkinson disease (PD) motor subtypes, including Tremor Dominant (TD) and Postural Instability/Gait Difficulty (PIGD) forms.METHODSIn 3,212 PD cases of European ancestry, we performed a genome-wide association study (GWAS) examining two complementary outcome traits derived from the Unified Parkinson’s Disease Rating Scale (UPDRS), including dichotomous motor subtype (TD vs. PIGD) or a continuous tremor / PIGD score ratio. Logistic or linear regression models were adjusted for sex, age of onset, disease duration, and 5 ancestry principal components, followed by meta-analysis.RESULTSAmong 71 established PD risk variants, we detected multiple suggestive associations with PD motor subtype, including GPNMB (rs199347, psubtype = 0.01, pratio = 0.03), SH3GL2 (rs10756907, psubtype = 0.02, pratio = 0.01), HIP1R (rs10847864, psubtype = 0.02), RIT2 (rs12456492, psubtype = 0.02), and FBRSL1 (rs11610045, psubtype = 0.02). A PD genetic risk score integrating all 71 PD risk variants was also associated with subtype ratio (p = 0.026, ß = −0.04, 95% CI = −0.07, 0). Based on top results of our GWAS, we identify a novel suggestive association at the STK32B locus (rs2301857, pratio = 6.6×10−7), which harbors an independent risk allele for essential tremor.CONCLUSIONSMultiple PD risk alleles may also modify clinical manifestations to influence PD motor subtype. The discovery of a novel variant at STK32B suggests a possible overlap between genetic risk for essential tremor and tremor-dominant PD.

MotivationThe CRISPR-based lineage tracing system is emerging as a powerful new sequencing tool to track cell lineages by marking cells with irreversible genetic mutations. Accurate reconstruction of cell lineages from CRISPR-based data is sensitive to noise. Quality control is critical for filtering out low-quality data points. Yet, existing quality control tools for RNA-seq and DNA-seq do not measure features specific for the CRISPR-linear tracing system.ResultsWe introduce TraceQC, a quality control package to overcome challenges with measuring the quality of CRISPR-based lineage tracing data and help in interpreting and constructing lineage trees.AvailabilityThe R package is available at https://github.com/LiuzLab/TraceQC.Contactzhandong.liu@bcm.edu or hyun-hwan.jeong@bcm.edu

Data mining algorithms and sequencing methods (such as RNA-seq and ChIP-seq) are being combined to discover genomic regulatory motifs that relate to a variety of phenotypes. However, motif discovery algorithms often produce very long lists of putative transcription factor binding sites, hindering the discovery of phenotype-related regulatory elements by making it difficult to select a manageable set of candidate motifs for experimental validation. To address this issue, the authors introduce the motif selection problem and provide coverage-based search heuristics for its solution. Analysis of 203 ChIP-seq experiments from the ENCyclopedia of DNA Elements project shows that our algorithms produce motifs that have high sensitivity and specificity and reveals new insights about the regulatory code of the human genome. The greedy algorithm performs the best, selecting a median of two motifs per ChIP-seq transcription factor group while achieving a median sensitivity of 77 percent.

Metastasis has been considered as the terminal step of tumor progression. However, recent genomic studies suggest that many metastases are initiated by further spread of other metastases. Nevertheless, the corresponding pre-clinical models are lacking, and underlying mechanisms are elusive. Using several approaches, including parabiosis and an evolving barcode system, we demonstrated that the bone microenvironment facilitates breast and prostate cancer cells to further metastasize and establish multi-organ secondary metastases. We uncovered that this metastasis-promoting effect is driven by epigenetic reprogramming that confers stem cell-like properties on cancer cells disseminated from bone lesions. Furthermore, we discovered that enhanced EZH2 activity mediates the increased stemness and metastasis capacity. The same findings also apply to single cell-derived populations, indicating mechanisms distinct from clonal selection. Taken together, our work revealed an unappreciated role of the bone microenvironment in metastasis evolution and elucidated an epigenomic reprogramming process driving terminal-stage, multi-organ metastases.

Target nomination for drug development has been a major challenge in the path to finding a cure for several neurological disorders. Comprehensive transcriptome profiles have revealed brain gene expression changes associated with many neurological disorders, and the functional validation of these changes is a critical next step. Model organisms are a proven approach for the elucidation of disease mechanisms, including screening of gene candidates as therapeutic targets. Frequently, multiple models exist for a given disease, creating a challenge to select the optimal model for validation and functional follow-up. To help in nominating the best mouse models for studying neurological diseases, we developed a web portal to visualize mouse transcriptomic data related to neurological disorders: http://mmad.nrihub.org. Users can examine gene expression changes across mouse model studies to help select the optimal mouse model for further investigation. The portal provides access to mouse studies related to Alzheimer&rsquo, s diseases (AD), Parkinson&rsquo, s disease (PD), Huntington&rsquo, s disease (HD), Amyotrophic Lateral Sclerosis (ALS), Spinocerebellar ataxia (SCA), and models related to aging.

ObjectiveTo discover genetic determinants of Parkinson disease (PD) motor subtypes, including tremor dominant (TD) and postural instability/gait difficulty (PIGD) forms.MethodsIn 3,212 PD cases of European ancestry, we performed a genome-wide association study (GWAS) examining 2 complementary outcome traits derived from the Unified Parkinson's Disease Rating Scale, including dichotomous motor subtype (TD vs PIGD) or a continuous tremor/PIGD score ratio. Logistic or linear regression models were adjusted for sex, age at onset, disease duration, and 5 ancestry principal components, followed by meta-analysis.ResultsAmong 71 established PD risk variants, we detected multiple suggestive associations with PD motor subtype, including GPNMB (rs199351, psubtype = 0.01, pratio = 0.03), SH3GL2 (rs10756907, psubtype = 0.02, pratio = 0.01), HIP1R (rs10847864, psubtype = 0.02), RIT2 (rs12456492, psubtype = 0.02), and FBRSL1 (rs11610045, psubtype = 0.02). A PD genetic risk score integrating all 71 PD risk variants was also associated with subtype ratio (p = 0.026, ß = −0.04, 95% confidence interval = −0.07–0). Based on top results of our GWAS, we identify a novel suggestive association at the STK32B locus (rs2301857, pratio = 6.6 × 10−7), which harbors an independent risk allele for essential tremor.ConclusionsMultiple PD risk alleles may also modify clinical manifestations to influence PD motor subtype. The discovery of a novel variant at STK32B suggests a possible overlap between genetic risk for essential tremor and tremor-dominant PD.

SUMMARYHuman brain transcriptomes can highlight biological pathways associated with Alzheimer’s disease (AD); however, challenges remain to link expression changes with causal triggers. We have examined 30 AD-associated, gene coexpression modules from human brains for overlap with 251 differentially-expressed gene sets from mouse brain RNA-sequencing experiments, including from models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus neurofibrillary tangle pathology and further reveal age- and sex-dependent expression signatures for AD progression. Human coexpression modules enriched for neuronal and/or microglial genes overlap broadly with signatures from mouse models of AD, Huntington’s disease, Amyotrophic Lateral Sclerosis, and also aging. Several human AD coexpression modules, including those implicated in the unfolded protein response and oxidative phosphorylation, were not activated in AD models, but instead were detected following other, unexpected mouse genetic manipulations. Our results comprise a powerful, cross-species resource and pinpoint experimental models for diverse features of AD pathophysiology from human brain transcriptomes.

In Alzheimer’s disease (AD), spliceosomal proteins with critical roles in RNA processing aberrantly aggregate and mislocalize to Tau neurofibrillary tangles. We test the hypothesis that Tau-spliceosome interactions disrupt pre-mRNA splicing in AD. In human postmortem brain with AD pathology, Tau coimmunoprecipitates with spliceosomal core components. In Drosophila models, pan-neuronal Tau expression triggers reductions in core and U1-specific spliceosomal proteins, and genetic disruption of these factors, including SmB, U1-70K, and U1A, enhances Tau-mediated neurodegeneration. We further show that loss-of-function in SmB, encoding a core spliceosomal protein, causes decreased survival, progressive locomotor impairment, and neuronal loss, independent of Tau toxicity. Lastly, RNA-sequencing reveals a similar profile of mRNA splicing errors in SmB mutant and Tau transgenic flies, including intron retention and non-annotated cryptic splice junctions. In human brains, we confirm cryptic splicing errors in association with neurofibrillary tangle pathologic burden. Our results implicate spliceosome disruption and perturbations of the neuronal transcriptome in Tau-mediated neurodegeneration in AD.

Dual Leucine Zipper Kinase (DLK, Map3k12), is an injury-induced axonal protein that governs the balance between degeneration and regeneration through its downstream effectors c-jun N-terminal kinase (JNK) and phosphorylated c-jun (p-c-Jun). DLK is generally considered to be inactive in healthy neurons until induced by injury. However we report that DLK in the cerebellum appears constitutively active and drives nuclear p-c-Jun in cerebellar granule neurons in the absence of injury. In contrast the adult hippocampus expresses similar levels of apparently constitutively active DLK, but p-c-Jun is lower and does not accumulate in the nucleus. Injury is required there for p-c-jun nuclear expression, because in the rTg4510 model of tauopathy, where there is extensive hippocampal pathology, nuclear p-c-Jun is induced in a DLK-dependent manner. This context-specific regulation of DLK signaling could relate to availability of JNK scaffolding proteins, as the cerebellum preferentially expresses JNK-interacting protein-1 (JIP-1) whereas the hippocampus contains more JIP-3 and Plenty of SH3 (POSH). To understand how DLK signaling differs between the hippocampus and cerebellum, we selectively blocked DLK and measured changes in protein and mRNA expression. In the cerebellum, p-c-Jun levels correlated with synaptophysin, suggesting a link between DLK activity and synaptic maintenance. In rTg4510 mice, hippocampal p-c-Jun instead correlated with markers of neuronal injury and gliosis (Iba1 and GFAP). RNA sequencing revealed that in both brain regions DLK inhibition reduced expression of JNK/c-Jun pathway components and a novel set of co-regulated genes. In the cerebellum, Jun mRNA levels were co-regulated with genes mapping to metabolic pathways, while in the rTg4510 hippocampus, Jun-correlated mRNAs correspond primarily to neuroinflammation. These data suggest that in the uninjured cerebellum, DLK/p-c-Jun signaling is linked to synaptic regulation, but in the hippocampus, pathologically activated DLK/p-c-Jun signaling regulates genes associated with the injury response.

One major challenge encountered with interpreting human genetic variants is the limited understanding of the functional impact of genetic alterations on biological processes. Furthermore, there remains an unmet demand for an efficient survey of the wealth of information on human homologs in model organisms across numerous databases. To efficiently assess the large volume of publically available information, it is important to provide a concise summary of the most relevant information in a rapid user-friendly format. To this end, we created MARRVEL (model organism aggregated resources for rare variant exploration). MARRVEL is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER. Importantly, it curates model organism-specific databases to concurrently display a concise summary regarding the human gene homologs in budding and fission yeast, worm, fly, fish, mouse, and rat on a single webpage. Experiment-based information on tissue expression, protein subcellular localization, biological process, and molecular function for the human gene and homologs in the seven model organisms are arranged into a concise output. Hence, rather than visiting multiple separate databases for variant and gene analysis, users can obtain important information by searching once through MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research.

Background An important focus of genomic science is the discovery and characterization of all functional elements within genomes. In silico methods are used in genome studies to discover putative regulatory genomic elements (called words or motifs). Although a number of methods have been developed for motif discovery, most of them lack the scalability needed to analyze large genomic data sets. Methods This manuscript presents WordSeeker, an enumerative motif discovery toolkit that utilizes multi-core and distributed computational platforms to enable scalable analysis of genomic data. A controller task coordinates activities of worker nodes, each of which (1) enumerates a subset of the DNA word space and (2) scores words with a distributed Markov chain model. Results A comprehensive suite of performance tests was conducted to demonstrate the performance, speedup and efficiency of WordSeeker. The scalability of the toolkit enabled the analysis of the entire genome of Arabidopsis thaliana; the results of the analysis were integrated into The Arabidopsis Gene Regulatory Information Server (AGRIS). A public version of WordSeeker was deployed on the Glenn cluster at the Ohio Supercomputer Center. Conclusion WordSeeker effectively utilizes concurrent computing platforms to enable the identification of putative functional elements in genomic data sets. This capability facilitates the analysis of the large quantity of sequenced genomic data.