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Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and social interaction and restricted, repetitive patterns of behavior, interests, or activities. Given the lack of specific pharmacological therapy for ASD and the clinical heterogeneity of the disorder, current biomarker research efforts are geared mainly toward identifying markers for determining ASD risk or for assisting with a diagnosis. A wide range of putative biological markers for ASD is currently being investigated. Proteomic analyses indicate that the levels of many proteins in plasma/serum are altered in ASD, suggesting that a panel of proteins may provide a blood biomarker for ASD. Serum samples from 76 boys with ASD and 78 typically developing (TD) boys, 18 months-8 years of age, were analyzed to identify possible early biological markers for ASD. Proteomic analysis of serum was performed using SomaLogic’s SOMAScan TM assay 1.3K platform. A total of 1,125 proteins were analyzed. There were 86 downregulated proteins and 52 upregulated proteins in ASD (FDR < 0.05). Combining three different algorithms, we found a panel of 9 proteins that identified ASD with an area under the curve (AUC) = 0.8599±0.0640, with specificity and sensitivity of 0.8217±0.1178 and 0.835±0.1176, respectively. All 9 proteins were significantly different in ASD compared with TD boys, and were significantly correlated with ASD severity as measured by ADOS total scores. Using machine learning methods, a panel of serum proteins was identified that may be useful as a blood biomarker for ASD in boys. Further verification of the protein biomarker panel with independent test sets is warranted.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and social interaction and restricted, repetitive patterns of behavior, interests, or activities. Given the lack of specific pharmacological therapy for ASD and the clinical heterogeneity of the disorder, current biomarker research efforts are geared mainly toward identifying markers for determining ASD risk or for assisting with a diagnosis. A wide range of putative biological markers for ASD are currently being investigated. Proteomic analyses indicate that the levels of many proteins in plasma/serum are altered in ASD, suggesting that a panel of proteins may provide a blood biomarker for ASD. Serum samples from 76 boys with ASD and 78 typically developing (TD) boys, 2–10 years of age, were analyzed to identify possible early biological markers for ASD. Proteomic analysis of serum was performed using SomaLogic’s SOMAScan TM assay 1.3K platform. A total of 1,125 proteins were analyzed. There were 86 downregulated proteins and 52 upregulated proteins in ASD (FDR < 0.05). Combining three different algorithms, we found a panel of 12 proteins that identified ASD with an area under the curve (AUC) = 0.8790±0.0572, with specificity and sensitivity of 0.8530±0.1076 and 0.8324±0.1137, respectively. All 12 proteins were significantly different in ASD compared with TD boys, and 4 were significantly correlated with ASD severity as measured by ADOS total scores. Using machine learning methods, a panel of serum proteins was identified that may be useful as a blood biomarker for ASD in boys. Further verification of the protein biomarker panel with independent test sets is warranted.

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk tissues. However, such bulk measurement lacks spatial resolution and obscures tissue heterogeneity, precluding proteome mapping of tissue microenvironment. Here we report an integrated w et c ollection of single microscale tissue voxels and S urfactant-assisted O ne- P ot voxel processing method termed wcSOP for robust label-free single voxel proteomics. wcSOP capitalizes on buffer droplet-assisted wet collection of single voxels dissected by LCM to the tube cap and SOP voxel processing in the same collection cap. This method enables reproducible, label-free quantification of approximately 900 and 4600 proteins for single voxels at 20 µm × 20 µm × 10 µm (~1 cell region) and 200 µm × 200 µm × 10 µm (~100 cell region) from fresh frozen human spleen tissue, respectively. It can reveal spatially resolved protein signatures and region-specific signaling pathways. Furthermore, wcSOP-MS is demonstrated to be broadly applicable for OCT-embedded and FFPE human archived tissues as well as for small-scale 2D proteome mapping of tissues at high spatial resolutions. wcSOP-MS may pave the way for routine robust single voxel proteomics and spatial proteomics. Proteome mapping of tissues is crucial for phenotypic characterization of tissue heterogeneity and microenvironment within spatial context. Here the authors report a robust, easy-to-use single voxel proteomics technique for deep proteome mapping of tissues and profiling of regions of interest.

Prune belly syndrome (PBS), also known as Eagle-Barret syndrome, is a rare, multi-system congenital myopathy primarily affecting males. Phenotypically, PBS cases manifest three cardinal pathological features: urinary tract dilation with poorly contractile smooth muscle, wrinkled flaccid ventral abdominal wall with skeletal muscle deficiency, and intra-abdominal undescended testes. Genetically, PBS is poorly understood. After performing whole exome sequencing in PBS patients, we identify one compound heterozygous variant in the PIEZO1 gene. PIEZO1 is a cation-selective channel activated by various mechanical forces and widely expressed throughout the lower urinary tract. Here we conduct an extensive functional analysis of the PIEZO1 PBS variants that reveal loss-of-function characteristics in the pressure-induced normalized open probability (NPo) of the channel, while no change is observed in single-channel currents. Furthermore, Yoda1, a PIEZO1 activator, can rescue the NPo defect of the PBS mutant channels. Thus, PIEZO1 mutations may be causal for PBS and the in vitro cellular pathophysiological phenotype could be rescued by the small molecule, Yoda1. Activation of PIEZO1 might provide a promising means of treating PBS and other related bladder dysfunctional states. PIEZO1 is a mechanosensitive ion channel. Here, authors identify PIEZO1 human mutations in Prune Belly Syndrome. At a single molecule level these mutations exhibit loss-of-function characteristics.

Tumor-initiating cells with reprogramming plasticity or stem-progenitor cell properties (stemness) are thought to be essential for cancer development and metastatic regeneration in many cancers; however, elucidation of the underlying molecular network and pathways remains demanding. Combining machine learning and experimental investigation, here we report CD81, a tetraspanin transmembrane protein known to be enriched in extracellular vesicles (EVs), as a newly identified driver of breast cancer stemness and metastasis. Using protein structure modeling and interface prediction-guided mutagenesis, we demonstrate that membrane CD81 interacts with CD44 through their extracellular regions in promoting tumor cell cluster formation and lung metastasis of triple negative breast cancer (TNBC) in human and mouse models. In-depth global and phosphoproteomic analyses of tumor cells deficient with CD81 or CD44 unveils endocytosis-related pathway alterations, leading to further identification of a quality-keeping role of CD44 and CD81 in EV secretion as well as in EV-associated stemness-promoting function. CD81 is coexpressed along with CD44 in human circulating tumor cells (CTCs) and enriched in clustered CTCs that promote cancer stemness and metastasis, supporting the clinical significance of CD81 in association with patient outcomes. Our study highlights machine learning as a powerful tool in facilitating the molecular understanding of new molecular targets in regulating stemness and metastasis of TNBC.

Bioinformatics analysis is a key element in the development of in-house next-generation sequencing assays for tumor genetic profiling that can include both tumor DNA and RNA with comparisons to matched-normal DNA in select cases. Bioinformatics analysis encompasses a computationally heavy component that requires a high-performance computing component and an assay-dependent quality assessment, aggregation, and data cleaning component. Although there are free, open-source solutions and fee-for-use commercial services for the computationally heavy component, these solutions and services can lack the options commonly utilized in increasingly complex genomic assays. Additionally, the cost to purchase commercial solutions or implement and maintain open-source solutions can be out of reach for many small clinical laboratories. Here, we present Software for Clinical Health in Oncology for Omics Laboratories (SCHOOL), a collection of genomics analysis workflows that (i) can be easily installed on any platform; (ii) run on the cloud with a user-friendly interface; and (iii) include the detection of single nucleotide variants, insertions/deletions, copy number variants (CNVs), and translocations from RNA and DNA sequencing. These workflows contain elements for customization based on target panel and assay design, including somatic mutational analysis with a matched-normal, microsatellite stability analysis, and CNV analysis with a single nucleotide polymorphism backbone. All of the features of SCHOOL have been designed to run on any computer system, where software dependencies have been containerized. SCHOOL has been built into apps with workflows that can be run on a cloud platform such as DNANexus using their point-and-click graphical interface, which could be automated for high-throughput laboratories.

Background: The presence of mild traumatic brain injury (mTBI) increases post-traumatic stress disorder (PTSD) symptoms in the months following injury. However, factors that link mTBI and PTSD development are still unclear. Acute stress responses after trauma have been associated with PTSD development. mTBI may impair cognitive functions and increase anxiety immediately after trauma. Objective: This research aimed to test the possibility that mTBI increases acute stress symptoms rapidly, which in turn results in PTSD development in the subsequent months. Method: Fifty-nine patients were recruited from the emergency rooms of local hospitals. Post-mTBI, acute stress, and PTSD symptom severity were measured using the Rivermead Post-Concussion Symptoms Questionnaire (RPQ), Acute Stress Disorder Scale (ASDS), and PTSD Checklist for DSM-5 (PCL-5), respectively. Results: Moderated mediation analysis indicated that ASDS, at 2 weeks post-trauma, mediated the relationship between RPQ scores at 2 weeks and PCL-5 scores at 3 months post-trauma, only for patients who met mTBI diagnostic criteria. Conclusions: These findings present preliminary evidence suggesting that acute stress disorder symptoms may be one of the mechanisms involved in the development of PTSD among trauma survivors who have experienced mTBI, which provides a theoretical basis for early intervention of PTSD prevention after mTBI.

This thesis will seek to prove the non-existence of certain cases of edge-regular graphs of the form ( , , ) with the parameters of = 3 + 3 − 6, where > 0 and ≥ 0, given a strict structural limitation that for each ∈ , the set = ( , ) induces a disjoint union of / 2 2. In edge-regular graphs of this form, is the number of vertices (or the cardinality of the vertex set, i.e. | | = ), is the number of common neighbors of any two adjacent vertices (i.e. | ( ) ∩ ( )| = , given ∈ ), and is the number of common non-neighbors of any two adjacent vertices (i.e. = − | ( ) ∪ ( )|, given ∈ ). This thesis will be comprised of two main sections: an introduction and terminology section, and a section for results and proofs. This work is building on the previous work in this area by Johnson, McNellis, Parker, and Roblee, who published results dealing with edge-regular graphs with the same structural limitations and parameters of = 3 + 3 − 2 and = 3 + 3 − 4 ( > 0, ≥ 0).

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk tissues. However, such bulk measurement lacks spatial resolution and obscures important tissue heterogeneity, which make it impossible for proteome mapping of tissue microenvironment. Here we report an integrated wet collection of single tissue voxel and Surfactant-assisted One-Pot voxel processing method termed wcSOP for robust label-free single voxel proteomics. wcSOP capitalizes on buffer droplet-assisted wet collection of single tissue voxel dissected by LCM into the PCR tube cap and MS-compatible surfactant-assisted one-pot voxel processing in the collection cap. This convenient method allows reproducible label-free quantification of ∼900 and ∼4,600 proteins for single voxel from fresh frozen human spleen tissue at 20 μm × 20 μm × 10 μm (close to single cells) and 200 μm × 200 μm × 10 μm (∼100 cells), respectively. 100s-1000s of protein signatures with differential expression levels were identified to be spatially resolved between spleen red and white pulp regions depending on the voxel size. Region-specific signaling pathways were enriched from single voxel proteomics data. Antibody-based CODEX imaging was used to validate label-free MS quantitation for single voxel analysis. The wcSOP-MS method paves the way for routine robust single voxel proteomics and spatial proteomics.

The woolly mammoth (Mammuthus primigenius) possessed a thick woolly coat and other cold-adaptive traits that enabled survival in harsh Arctic environments. Current de-extinction efforts focus on genetically modifying the closely related Asian elephant to express woolly mammoth traits. In this study, we establish a multiplex-edited mouse model with modifications in genes associated with hair morphology and lipid metabolism, enabling insights into traits involved in developing woolly hair and cold tolerance. Our optimized workflows achieved high editing efficiencies and produced genetically modified mice with simultaneous editing of up to seven different genes. Selected modifications include loss-of-function mutations in Fgf5, Tgm3, and Fam83g, among others. The resulting mice display exaggerated hair phenotypes including curly, textured coats, and golden-brown hair. This study establishes a rapid platform for testing mammoth-centric genetic variants while advancing methods for complex genetic model generation. These approaches inform de-extinction efforts and research into the genetic basis of mammalian hair development and cold adaptation.Competing Interest StatementThe authors have filed a patent application based on the results of this work. All authors are current or former employees, or scientific advisors/consultants for Colossal Biosciences and/or Form Bio, and may hold stock and/or stock options in these companies. G.M.C. is a founder and shareholder of Colossal Biosciences and others - full disclosure for G.M.C. is available at http://arep.med.harvard.edu/gmc/tech.html.