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Thesis ; Online: Multiscale Approaches to Complex Human Diseases

Beckmann, Noam D.

2018

Abstract: Elucidating the fundamental biological mechanisms underlying complex diseases remains, to date, a challenging task. Genetic approaches have been used to implicate genes and pathways in disease, but classical (reverse) and population (forward) genetic ... ...

Abstract	Elucidating the fundamental biological mechanisms underlying complex diseases remains, to date, a challenging task. Genetic approaches have been used to implicate genes and pathways in disease, but classical (reverse) and population (forward) genetic approaches have fallen short of bringing new understanding to multi-factorial disorders. With the emergence of rapidly advancing, new technologies and the dramatic reduction of their costs, new algorithms and analytical models that maximally leverage the data these technologies generate, are being developed to uncover novel biological insights for complex traits. Furthermore, the integration of multiple strata of biological information permits the creation of better representations of disease. This thesis aims to leverage some of these models on novel multi-scale datasets to explore the mechanisms of Alzheimer’s disease and the variability of induced pluripotent stem cells.
Keywords	Genetics\|Systematic biology\|Bioinformatics
Language	ENG
Publishing date	2018-01-01 00:00:01.0
Publisher	Icahn School of Medicine at Mount Sinai
Publishing country	us
Document type	Thesis ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Characterizing cell type specific transcriptional differences between the living and postmortem human brain.

Vornholt, Eric / Liharska, Lora E / Cheng, Esther / Hashemi, Alice / Park, You Jeong / Ziafat, Kimia / Wilkins, Lillian / Silk, Hannah / Linares, Lisa M / Thompson, Ryan C / Sullivan, Brendan / Moya, Emily / Nadkarni, Girish N / Sebra, Robert / Schadt, Eric E / Kopell, Brian H / Charney, Alexander W / Beckmann, Noam D

medRxiv : the preprint server for health sciences

2024

Abstract: Single-nucleus RNA sequencing (snRNA-seq) is often used to define gene expression patterns characteristic of brain cell types as well as to identify cell type specific gene expression signatures of neurological and mental illnesses in postmortem human ... ...

Abstract	Single-nucleus RNA sequencing (snRNA-seq) is often used to define gene expression patterns characteristic of brain cell types as well as to identify cell type specific gene expression signatures of neurological and mental illnesses in postmortem human brains. As methods to obtain brain tissue from living individuals emerge, it is essential to characterize gene expression differences associated with tissue originating from either living or postmortem subjects using snRNA-seq, and to assess whether and how such differences may impact snRNA-seq studies of brain tissue. To address this, human prefrontal cortex single nuclei gene expression was generated and compared between 31 samples from living individuals and 21 postmortem samples. The same cell types were consistently identified in living and postmortem nuclei, though for each cell type, a large proportion of genes were differentially expressed between samples from postmortem and living individuals. Notably, estimation of cell type proportions by cell type deconvolution of pseudo-bulk data was found to be more accurate in samples from living individuals. To allow for future integration of living and postmortem brain gene expression, a model was developed that quantifies from gene expression data the probability a human brain tissue sample was obtained postmortem. These probabilities are established as a means to statistically account for the gene expression differences between samples from living and postmortem individuals. Together, the results presented here provide a deep characterization of both differences between snRNA-seq derived from samples from living and postmortem individuals, as well as qualify and account for their effect on common analyses performed on this type of data.
Language	English
Publishing date	2024-05-01
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.05.01.24306590
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Article: Dual-specificity protein phosphatase 6 (DUSP6) overexpression reduces amyloid load and improves memory deficits in male 5xFAD mice.

Pan, Allen L / Audrain, Mickael / Sakakibara, Emmy / Joshi, Rajeev / Zhu, Xiaodong / Wang, Qian / Wang, Minghui / Beckmann, Noam D / Schadt, Eric E / Gandy, Sam / Zhang, Bin / Ehrlich, Michelle E / Salton, Stephen R

bioRxiv : the preprint server for biology

2023

Abstract: Background: Dual specificity protein phosphatase 6 (DUSP6) was recently identified as a key hub gene in a causal network that regulates late-onset Alzheimer's disease. Importantly, decreased DUSP6 levels are correlated with an increased clinical ... ...

Abstract	Background: Dual specificity protein phosphatase 6 (DUSP6) was recently identified as a key hub gene in a causal network that regulates late-onset Alzheimer's disease. Importantly, decreased DUSP6 levels are correlated with an increased clinical dementia rating in human subjects, and DUSP6 levels are additionally decreased in the 5xFAD amyloidopathy mouse model. Methods: AAV5-DUSP6 or AAV5-GFP (control) were stereotactically injected into the dorsal hippocampus (dHc) of female and male 5xFAD or wild type mice to overexpress DUSP6 or GFP. Spatial learning memory of these mice was assessed in the Barnes maze, after which hippocampal tissues were isolated for downstream analysis. Results: Barnes maze testing indicated that DUSP6 overexpression in the dHc of 5xFAD mice improved memory deficits and was associated with reduced amyloid plaque load, Aß Conclusions: In summary, our data indicate that DUSP6 overexpression in dHc reduced amyloid deposition and memory deficits in male but not female 5xFAD mice, whereas reduced neuroinflammation and microglial activation were observed in both males and females. The sex-dependent regulation of synaptic pathways by DUSP6 overexpression, however, correlated with the improvement of spatial memory deficits in male but not female 5xFAD.
Language	English
Publishing date	2023-08-25
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.08.24.554335
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Article ; Online: Dual-Specificity Protein Phosphatase 4 (DUSP4) Overexpression Improves Learning Behavior Selectively in Female 5xFAD Mice, and Reduces β-Amyloid Load in Males and Females.

Cells

2022 Volume 11, Issue 23

Abstract: Recent multiscale network analyses of banked brains from subjects who died of late-onset sporadic Alzheimer's disease converged ... ...

Abstract	Recent multiscale network analyses of banked brains from subjects who died of late-onset sporadic Alzheimer's disease converged on
MeSH term(s)	Animals ; Female ; Male ; Mice ; Alzheimer Disease/genetics ; Alzheimer Disease/pathology ; Amyloid beta-Peptides/metabolism ; Extracellular Signal-Regulated MAP Kinases/metabolism ; Hippocampus/metabolism ; Protein Tyrosine Phosphatases/genetics ; Learning
Chemical Substances	Amyloid beta-Peptides ; Extracellular Signal-Regulated MAP Kinases (EC 2.7.11.24) ; MKP2 protein, mouse (EC 3.1.3.48) ; Protein Tyrosine Phosphatases (EC 3.1.3.48)
Language	English
Publishing date	2022-12-01
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2661518-6
ISSN	2073-4409 ; 2073-4409
ISSN (online)	2073-4409
ISSN	2073-4409
DOI	10.3390/cells11233880
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Persistent complement dysregulation with signs of thromboinflammation in active Long Covid.

Cervia-Hasler, Carlo / Brüningk, Sarah C / Hoch, Tobias / Fan, Bowen / Muzio, Giulia / Thompson, Ryan C / Ceglarek, Laura / Meledin, Roman / Westermann, Patrick / Emmenegger, Marc / Taeschler, Patrick / Zurbuchen, Yves / Pons, Michele / Menges, Dominik / Ballouz, Tala / Cervia-Hasler, Sara / Adamo, Sarah / Merad, Miriam / Charney, Alexander W /

Puhan, Milo / Brodin, Petter / Nilsson, Jakob / Aguzzi, Adriano / Raeber, Miro E / Messner, Christoph B / Beckmann, Noam D / Borgwardt, Karsten / Boyman, Onur

Science (New York, N.Y.)

2024 Volume 383, Issue 6680, Page(s) eadg7942

Abstract: Long Covid is a debilitating condition of unknown etiology. We performed multimodal proteomics analyses of blood serum from COVID-19 patients followed up to 12 months after confirmed severe acute respiratory syndrome coronavirus 2 infection. Analysis of > ...

Abstract	Long Covid is a debilitating condition of unknown etiology. We performed multimodal proteomics analyses of blood serum from COVID-19 patients followed up to 12 months after confirmed severe acute respiratory syndrome coronavirus 2 infection. Analysis of >6500 proteins in 268 longitudinal samples revealed dysregulated activation of the complement system, an innate immune protection and homeostasis mechanism, in individuals experiencing Long Covid. Thus, active Long Covid was characterized by terminal complement system dysregulation and ongoing activation of the alternative and classical complement pathways, the latter associated with increased antibody titers against several herpesviruses possibly stimulating this pathway. Moreover, markers of hemolysis, tissue injury, platelet activation, and monocyte-platelet aggregates were increased in Long Covid. Machine learning confirmed complement and thromboinflammatory proteins as top biomarkers, warranting diagnostic and therapeutic interrogation of these systems.
MeSH term(s)	Humans ; Complement Activation ; Complement System Proteins/analysis ; Complement System Proteins/metabolism ; Post-Acute COVID-19 Syndrome/blood ; Post-Acute COVID-19 Syndrome/complications ; Post-Acute COVID-19 Syndrome/immunology ; Thromboinflammation/blood ; Thromboinflammation/immunology ; Biomarkers/blood ; Proteome ; Proteomics ; Male ; Female ; Young Adult ; Adult ; Middle Aged ; Aged
Chemical Substances	Complement System Proteins (9007-36-7) ; Biomarkers ; Proteome
Language	English
Publishing date	2024-01-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	128410-1
ISSN	1095-9203 ; 0036-8075
ISSN (online)	1095-9203
ISSN	0036-8075
DOI	10.1126/science.adg7942
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Article ; Online: Detecting epigenetic motifs in low coverage and metagenomics settings.

Beckmann, Noam D / Karri, Sashank / Fang, Gang / Bashir, Ali

BMC bioinformatics

2014 Volume 15 Suppl 9, Page(s) S16

Abstract: Background: It has recently become possible to rapidly and accurately detect epigenetic signatures in bacterial genomes using third generation sequencing data. Monitoring the speed at which a single polymerase inserts a base in the read strand enables ... ...

Abstract	Background: It has recently become possible to rapidly and accurately detect epigenetic signatures in bacterial genomes using third generation sequencing data. Monitoring the speed at which a single polymerase inserts a base in the read strand enables one to infer whether a modification is present at that specific site on the template strand. These sites can be challenging to detect in the absence of high coverage and reliable reference genomes. Methods: Here we provide a new method for detecting epigenetic motifs in bacteria on datasets with low-coverage, with incomplete references, and with mixed samples (i.e. metagenomic data). Our approach treats motif inference as a kmer comparison problem. First, genomes (or contigs) are deconstructed into kmers. Then, native genome-wide distributions of interpulse durations (IPDs) for kmers are compared with corresponding whole genome amplified (WGA, modification free) IPD distributions using log likelihood ratios. Finally, kmers are ranked and greedily selected by iteratively correcting for sequences within a particular kmer's neighborhood. Conclusions: Our method can detect multiple types of modifications, even at very low-coverage and in the presence of mixed genomes. Additionally, we are able to predict modified motifs when genomes with "neighbor" modified motifs exist within the sample. Lastly, we show that these motifs can provide an alternative source of information by which to cluster metagenomics contigs and that iterative refinement on these clustered contigs can further improve both sensitivity and specificity of motif detection. Availability: https://github.com/alibashir/EMMCKmer.
MeSH term(s)	Algorithms ; Bacteria/genetics ; Base Sequence ; Computer Simulation ; Epigenesis, Genetic ; Genome, Bacterial ; Metagenomics/methods ; Models, Genetic ; Nucleotide Motifs
Language	English
Publishing date	2014-09-10
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/1471-2105-15-S9-S16
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Article ; Online: A new molecular signature method for prediction of driver cancer pathways from transcriptional data.

Rykunov, Dmitry / Beckmann, Noam D / Li, Hui / Uzilov, Andrew / Schadt, Eric E / Reva, Boris

Nucleic acids research

2016 Volume 44, Issue 11, Page(s) e110

Abstract: Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of ... ...

Abstract	Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of driver pathways across a broad range of tumors, a systematic characterization of key driver pathways based on RNA data has not been undertaken. Here we introduce a new approach for predicting the status of driver cancer pathways based on signature functions derived from RNA sequencing data. To identify the driver cancer pathways of interest, we mined DNA variant data from TCGA and nominated driver alterations in seven major cancer pathways in breast, ovarian and colon cancer tumors. The activation status of these driver pathways were then characterized using RNA sequencing data by constructing classification signature functions in training datasets and then testing the accuracy of the signatures in test datasets. The signature functions differentiate well tumors with nominated pathway activation from tumors with no signs of activation: average AUC equals to 0.83. Our results confirm that driver genomic alterations are distinctively displayed at the transcriptional level and that the transcriptional signatures can generally provide an alternative to DNA sequencing methods in detecting specific driver pathways.
MeSH term(s)	Algorithms ; Biomarkers, Tumor ; Computational Biology/methods ; Databases, Genetic ; Female ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Genetic Markers ; Genomics/methods ; Humans ; Machine Learning ; Male ; Neoplasms/genetics ; Neoplasms/metabolism ; Signal Transduction ; Transcriptome
Chemical Substances	Biomarkers, Tumor ; Genetic Markers
Language	English
Publishing date	2016-06-20
Publishing country	England
Document type	Journal Article
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkw269
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Article ; Online: Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity.

Cell stem cell

2022 Volume 29, Issue 10, Page(s) 1505

Language	English
Publishing date	2022-09-20
Publishing country	United States
Document type	Published Erratum
ZDB-ID	2375354-7
ISSN	1875-9777 ; 1934-5909
ISSN (online)	1875-9777
ISSN	1934-5909
DOI	10.1016/j.stem.2022.08.011
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Article: A study of gene expression in the living human brain.

Liharska, Lora E / Park, You Jeong / Ziafat, Kimia / Wilkins, Lillian / Silk, Hannah / Linares, Lisa M / Vornholt, Eric / Sullivan, Brendan / Cohen, Vanessa / Kota, Prashant / Feng, Claudia / Cheng, Esther / Moya, Emily / Thompson, Ryan C / Johnson, Jessica S / Rieder, Marysia-Kolbe / Huang, Jia / Scarpa, Joseph / Hashemi, Alice /

Polanco, Jairo / Levin, Matthew A / Nadkarni, Girish N / Sebra, Robert / Crary, John / Schadt, Eric E / Beckmann, Noam D / Kopell, Brian H / Charney, Alexander W

medRxiv : the preprint server for health sciences

2023

Abstract: A goal of medical research is to determine the molecular basis of human brain health and illness. One way to achieve this goal is through observational studies of gene expression in human brain tissue. Due to the unavailability of brain tissue from ... ...

Abstract	A goal of medical research is to determine the molecular basis of human brain health and illness. One way to achieve this goal is through observational studies of gene expression in human brain tissue. Due to the unavailability of brain tissue from living people, most such studies are performed using tissue from postmortem brain donors. An assumption underlying this practice is that gene expression in the postmortem human brain is an accurate representation of gene expression in the living human brain. Here, this assumption - which, until now, had not been adequately tested - is tested by comparing human prefrontal cortex gene expression between 275 living samples and 243 postmortem samples. Expression levels differed significantly for nearly 80% of genes, and a systematic examination of alternative explanations for this observation determined that these differences are not a consequence of cell type composition, RNA quality, postmortem interval, age, medication, morbidity, symptom severity, tissue pathology, sample handling, batch effects, or computational methods utilized. Analyses integrating the data generated for this study with data from earlier landmark studies that used tissue from postmortem brain donors showed that postmortem brain gene expression signatures of neurological and mental illnesses, as well as of normal traits such as aging, may not be accurate representations of these gene expression signatures in the living brain. By using tissue from large cohorts living people, future observational studies of human brain biology have the potential to (1) determine the medical research questions that can be addressed using postmortem tissue as a proxy for living tissue and (2) expand the scope of medical research to include questions about the molecular basis of human brain health and illness that can only be addressed in living people (e.g., "What happens at the molecular level in the brain as a person experiences an emotion?").
Language	English
Publishing date	2023-08-01
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.04.21.23288916
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Article ; Online: Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness.

Carcamo-Orive, Ivan / Henrion, Marc Y R / Zhu, Kuixi / Beckmann, Noam D / Cundiff, Paige / Moein, Sara / Zhang, Zenan / Alamprese, Melissa / D'Souza, Sunita L / Wabitsch, Martin / Schadt, Eric E / Quertermous, Thomas / Knowles, Joshua W / Chang, Rui

PLoS computational biology

2020 Volume 16, Issue 12, Page(s) e1008491

Abstract: Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms ...

Abstract	Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited. Thus, new approaches are necessary to explore the genetic architecture of insulin resistance. To that end, we generated an iPSC library across the spectrum of insulin sensitivity in humans. RNA-seq based analysis of 310 induced pluripotent stem cell (iPSC) clones derived from 100 individuals allowed us to identify differentially expressed genes between insulin resistant and sensitive iPSC lines. Analysis of the co-expression architecture uncovered several insulin sensitivity-relevant gene sub-networks, and predictive network modeling identified a set of key driver genes that regulate these co-expression modules. Functional validation in human adipocytes and skeletal muscle cells (SKMCs) confirmed the relevance of the key driver candidate genes for insulin responsiveness.
MeSH term(s)	Gene Regulatory Networks ; Humans ; Induced Pluripotent Stem Cells/metabolism ; Insulin/metabolism ; Insulin Resistance/genetics
Chemical Substances	Insulin
Language	English
Publishing date	2020-12-23
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1008491
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