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Inflammatory bowel disease (IBD) is characterized by chronic inflammation in the gut. There is growing evidence in Crohn's disease (CD) of the existence of a preclinical period characterized by immunological changes preceding symptom onset that starts years before diagnosis. Gaining insight into this preclinical phase will allow disease prediction and prevention. Analysis of preclinical serum samples, up to 6 years before IBD diagnosis (from the PREDICTS cohort), revealed the identification of a unique glycosylation signature on circulating antibodies (IgGs) characterized by lower galactosylation levels of the IgG fragment crystallizable (Fc) domain that remained stable until disease diagnosis. This specific IgG2 Fc glycan trait correlated with increased levels of antimicrobial antibodies, specifically anti-Saccharomyces cerevisiae (ASCA), pinpointing a glycome-ASCA hub detected in serum that predates by years the development of CD. Mechanistically, we demonstrated that this agalactosylated glycoform of ASCA IgG, detected in the preclinical phase, elicits a proinflammatory immune pathway through the activation and reprogramming of innate immune cells, such as dendritic cells and natural killer cells, via an FcγR-dependent mechanism, triggering NF-κB and CARD9 signaling and leading to inflammasome activation. This proinflammatory role of ASCA was demonstrated to be dependent on mannose glycan recognition and galactosylation levels in the IgG Fc domain. The pathogenic properties of (anti-mannose) ASCA IgG were validated in vivo. Adoptive transfer of antibodies to mannan (ASCA) to recipient wild-type mice resulted in increased susceptibility to intestinal inflammation that was recovered in recipient FcγR-deficient mice. Here we identify a glycosylation signature in circulating IgGs that precedes CD onset and pinpoint a specific glycome-ASCA pathway as a central player in the initiation of inflammation many years before CD diagnosis. This pathogenic glyco-hub may constitute a promising new serum biomarker for CD prediction and a potential target for disease prevention., (© 2024. The Author(s).)

Clonal hematopoiesis of indeterminate potential (CHIP) is the presence of somatic mutations in myeloid and lymphoid malignancy genes in the blood cells of individuals without a hematologic malignancy. Inflammation is hypothesized to be a key mediator in the progression of CHIP to hematologic malignancy and patients with CHIP have a high prevalence of inflammatory diseases. This study aimed to identify the prevalence and characteristics of CHIP in patients with inflammatory bowel disease (IBD). We analyzed whole exome sequencing data from 587 Crohn's disease (CD), 441 ulcerative colitis (UC), and 293 non-IBD controls to assess CHIP prevalence and used logistic regression to study associations with clinical outcomes. Older UC patients (age>45) harbored increased myeloid-CHIP mutations compared to younger patients (age≤45) ( p =0.01). Lymphoid-CHIP was more prevalent in older IBD patients ( p =0.007). Young CD patients were found to have myeloid-CHIP with high-risk features. IBD patients with CHIP exhibited unique mutational profiles compared to controls. Steroid use was associated with increased CHIP ( p =0.05), while anti-TNF therapy was associated with decreased myeloid-CHIP ( p =0.03). Pathway enrichment analyses indicated overlap between CHIP genes, IBD phenotypes, and inflammatory pathways. Our findings underscore a connection between IBD and CHIP pathophysiology. Patients with IBD and CHIP had unique risk profiles especially among older UC patients and younger CD patients. These findings suggest distinct evolutionary pathways for CHIP in IBD and necessitate awareness among IBD providers and hematologists to identify patients potentially at risk for CHIP-related complications including malignancy, cardiovascular disease and acceleration of their inflammatory disease., Competing Interests: J.M reports receiving consulting fees from Incyte, SOBI, Novartis, Geron, AbbVie, Bristol Myers Squibb, GSK, MorphoSys, Galecto, Disc, Pfizer, Merck, Keros and PharmaEssentia; receiving research support from Incyte, SOBI, Geron, BMS, PharmaEssentia, AbbVie, Novartis, Kartos and Ajax. R.H reports receiving consulting fees from Potagonaist Therapeutics and Silence Therapeutics; receiving research support from Kymera, Karyopharm, Cellinkos, Kartos, and Dexcel Therapeutics. L.J.C. reports receiving consulting fees from Orchard Therapeutics and ORGANOIDSCIENCES; receiving research grants from Bristol Myers Squibb; receiving payment for lectures from Ferring Pharmaceuticals and Takeda. B.K.M reports receiving consulting fees from Cellarity. All other authors have no disclosures. No authors have any conflicts of interest to declare.

Protein phosphorylation involves the reversible modification of a protein (substrate) residue by another protein (kinase). Liquid chromatography-mass spectrometry studies are rapidly generating massive protein phosphorylation datasets across multiple conditions. Researchers then must infer kinases responsible for changes in phosphosites of each substrate. However, tools that infer kinase-substrate interactions (KSIs) are not optimized to interactively explore the resulting large and complex networks, significant phosphosites, and states. There is thus an unmet need for a tool that facilitates user-friendly analysis, interactive exploration, visualization, and communication of phosphoproteomics datasets. We present PhosNetVis, a web-based tool for researchers of all computational skill levels to easily infer, generate and interactively explore KSI networks in 2D or 3D by streamlining phosphoproteomics data analysis steps within a single tool. PhostNetVis lowers barriers for researchers in rapidly generating high-quality visualizations to gain biological insights from their phosphoproteomics datasets. It is available at: https://gumuslab.github.io/PhosNetVis/., Competing Interests: Declaration of interests The authors declare no competing interests.

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents., Competing Interests: Declaration of interests R. Sebra is currently a paid consultant and equity holder at GeneDx. L.C.C. is a founder and member of the board of directors of Agios Pharmaceuticals; is a founder and receives research support from Petra Pharmaceuticals; has equity in and consults for Cell Signaling Technologies, Volastra, Larkspur, and 1 Base Pharmaceuticals; and consults for Loxo-Lilly. J.L.J. has received consulting fees from Scorpion Therapeutics and Volastra Therapeutics. T.M.Y. is a co-founder and stockholder of DeStroke., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Cancer risk is influenced by inherited mutations, DNA replication errors, and environmental factors. However, the influence of genetic variation in immunosurveillance on cancer risk is not well understood. Leveraging population-level data from the UK Biobank and FinnGen, we show that heterozygosity at the human leukocyte antigen (HLA) -II loci is associated with reduced lung cancer risk in smokers. Fine-mapping implicated amino acid heterozygosity in the HLA -II peptide binding groove in reduced lung cancer risk, and single-cell analyses showed that smoking drives enrichment of proinflammatory lung macrophages and HLA -II+ epithelial cells. In lung cancer, widespread loss of HLA -II heterozygosity (LOH) favored loss of alleles with larger neopeptide repertoires. Thus, our findings nominate genetic variation in immunosurveillance as a critical risk factor for lung cancer.

High-density lipoprotein (HDL) nanoparticles promote endothelial cell (EC) function and suppress inflammation, but their utility in treating EC dysfunction has not been fully explored. Here, we describe a fusion protein named ApoA1-ApoM (A1M) consisting of apolipoprotein A1 (ApoA1), the principal structural protein of HDL that forms lipid nanoparticles, and ApoM, a chaperone for the bioactive lipid sphingosine 1-phosphate (S1P). A1M forms HDL-like particles, binds to S1P, and is signaling competent. Molecular dynamics simulations showed that the S1P-bound ApoM moiety in A1M efficiently activated EC surface receptors. Treatment of human umbilical vein ECs with A1M-S1P stimulated barrier function either alone or cooperatively with other barrier-enhancing molecules, including the stable prostacyclin analog iloprost, and suppressed cytokine-induced inflammation. A1M-S1P injection into mice during sterile inflammation suppressed neutrophil influx and inflammatory mediator secretion. Moreover, systemic A1M administration led to a sustained increase in circulating HDL-bound S1P and suppressed inflammation in a murine model of LPS-induced endotoxemia. We propose that A1M administration may enhance vascular endothelial barrier function, suppress cytokine storm, and promote resilience of the vascular endothelium.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data., Competing Interests: Declaration of interests F.A. is an inventor on a patent application related to SignatureAnalyzer-GPU filed by the Broad Institute and is an employee and shareholder of Illumina Inc. since 8 November 2021., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

During persistent antigen stimulation, such as in chronic infections and cancer, CD8 T cells differentiate into a hypofunctional programmed death protein 1-positive (PD-1 + ) exhausted state. Exhausted CD8 T cell responses are maintained by precursors (Tpex) that express the transcription factor T cell factor 1 (TCF-1) and high levels of the costimulatory molecule CD28. Here, we demonstrate that sustained CD28 costimulation is required for maintenance of antiviral T cells during chronic infection. Low-level CD28 engagement preserved mitochondrial fitness and self-renewal of Tpex, whereas stronger CD28 signaling enhanced glycolysis and promoted Tpex differentiation into TCF-1 neg exhausted CD8 T cells (Tex). Furthermore, enhanced differentiation by CD28 engagement did not reduce the Tpex pool. Together, these findings demonstrate that continuous CD28 engagement is needed to sustain PD-1 + CD8 T cells and suggest that increasing CD28 signaling promotes Tpex differentiation into more functional effector-like Tex, possibly without compromising long-term responses.

Lapses in inhibitory control have been linked to relapse in human drug addiction. Evidence suggests differences in inhibitory control depending on abstinence duration, but the underlying neural mechanisms remain unknown. We hypothesized that early abstinence (2-5 days) would be characterized by the strongest impairments of inhibitory control and most wide-spread deviations in resting-state functional connectivity of brain networks, while longer-term abstinence (>30 days) would be characterized by weaker impairments as compared to healthy controls. In this laboratory-based cross-sectional study, we compared individuals with Cocaine Use Disorder (iCUD) during early (cocaine urine-positive: N = 19, iCUD+; 32% female; mean age: 46.8 years) and longer-term abstinence (cocaine urine-negative: N = 29, iCUD-; 15% female; mean age: 46.6 years) to healthy controls (N = 33; 24% female; mean age: 40.9 years). We compared the groups on inhibitory control performance (Stop-Signal Task) and, using a whole-brain graph theory analysis (638 region parcellation) of functional magnetic resonance imaging (fMRI) data, we tested for group differences in resting-state brain function (local/global efficiency). We characterized how resting-state brain function was associated with inhibitory control performance within iCUD. Inhibitory control performance was worst in the early abstinence group, and intermediate in the longer-term abstinence group, as compared to the healthy control group (P < 0.01). More recent use of cocaine (CUD+ > CUD- > healthy controls) was characterized by decreased efficiency in fronto-temporal and subcortical networks (primarily in the salience, semantic, and basal ganglia networks) and increased efficiency in visual networks. Importantly, a similar functional connectivity pattern characterized impaired inhibitory control performance within iCUD (all brain analyses P < 0.05, FWE-corrected). Together, we demonstrated that a similar pattern of systematic and widespread deviations in resting-state brain efficiency, extending beyond the networks commonly investigated in human drug addiction, is linked to both abstinence duration and inhibitory control deficits in iCUD., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Recent studies show that rare, deleterious variants (RDVs) in certain genes are critical determinants of heritable cancer risk. To more comprehensively understand RDVs, we performed the largest-to-date germline variant calling analysis in a case-control setting for a multi-cancer association study from whole-exome sequencing data of 20,789 participants, split into discovery and validation cohorts. We confirm and extend known associations between cancer risk and germline RDVs in specific gene-sets, including DNA repair (OR = 1.50; p-value = 8.30e-07; 95% CI: 1.28-1.77), cancer predisposition (OR = 1.51; p-value = 4.58e-08; 95% CI: 1.30-1.75), and somatic cancer drivers (OR = 1.46; p-value = 4.04e-06; 95% CI: 1.24-1.72). Furthermore, personal RDV load in these gene-sets associated with increased risk, younger age of onset, increased M1 macrophages in tumor and, increased tumor mutational burden in specific cancers. Our findings can be used towards identifying high-risk individuals, who can then benefit from increased surveillance, earlier screening, and treatments that exploit their tumor characteristics, improving prognosis., (© 2023. The Author(s).)

We review research regarding the epidemiology, risk factors, genetic susceptibility, molecular pathology, and early detection of SCLC, a deadly tumor that accounts for 14% of lung cancers. We first summarize the changing incidences of SCLC globally and in the United States among males and females. We then review the established risk factor (i.e., tobacco smoking) and suspected nonsmoking-related risk factors for SCLC, and emphasize the importance of continued effort in tobacco control worldwide. Review of genetic susceptibility and molecular pathology suggests different molecular pathways in SCLC development compared with other types of lung cancer. Last, we comment on the limited utility of low-dose computed tomography screening in SCLC and on several promising blood-based molecular biomarkers as potential tools in SCLC early detection., (Copyright © 2022 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Conflicts and natural disasters affect entire populations of the countries involved and, in addition to the thousands of lives destroyed, have a substantial negative impact on the scientific advances these countries provide. The unprovoked invasion of Ukraine by Russia, the devastating earthquake in Turkey and Syria, and the ongoing conflicts in the Middle East are just a few examples. Millions of people have been killed or displaced, their futures uncertain. These events have resulted in extensive infrastructure collapse, with loss of electricity, transportation, and access to services. Schools, universities, and research centers have been destroyed along with decades' worth of data, samples, and findings. Scholars in disaster areas face short- and long-term problems in terms of what they can accomplish now for obtaining grants and for employment in the long run. In our interconnected world, conflicts and disasters are no longer a local problem but have wide-ranging impacts on the entire world, both now and in the future. Here, we focus on the current and ongoing impact of war on the scientific community within Ukraine and from this draw lessons that can be applied to all affected countries where scientists at risk are facing hardship. We present and classify examples of effective and feasible mechanisms used to support researchers in countries facing hardship and discuss how these can be implemented with help from the international scientific community and what more is desperately needed. Reaching out, providing accessible training opportunities, and developing collaborations should increase inclusion and connectivity, support scientific advancements within affected communities, and expedite postwar and disaster recovery., (© The Author(s) 2023. Published by Oxford University Press GigaScience.)

Background: A previous genome-wide association study identified several loci with genetic variants associated with prostate cancer survival time in two cohorts from Sweden. Whether these variants have an effect in other populations or if their effect is homogenous across the course of disease is unknown., Methods: These variants were genotyped in a cohort of 1,298 patients. Samples were linked with age, PSA level, Gleason score, cancer stage at surgery, and times from surgery to biochemical recurrence to death from prostate cancer. SNPs rs2702185 and rs73055188 were tested for association with prostate cancer-specific survival time using a multivariate Cox proportional hazard model. SNP rs2702185 was further tested for association with time to biochemical recurrence and time from biochemical recurrence to death with a multi-state model., Results: SNP rs2702185 at SMG7 was associated with prostate cancer-specific survival time, specifically the time from biochemical recurrence to prostate cancer death (HR, 2.5; 95% confidence interval, 1.4-4.5; P = 0.0014). Nine variants were in linkage disequilibrium (LD) with rs2702185; one, rs10737246, was found to be most likely to be functional based on LD patterns and overlap with open chromatin. Patterns of open chromatin and correlation with gene expression suggest that this SNP may affect expression of SMG7 in T cells., Conclusions: The SNP rs2702185 at the SMG7 locus is associated with time from biochemical recurrence to prostate cancer death, and its LD partner rs10737246 is predicted to be functional., Impact: These results suggest that future association studies of prostate cancer survival should consider various intervals over the course of disease., (©2022 American Association for Cancer Research.)

Background: The genetic factors that modulate risk for developing lung cancer have not been fully defined. Here, we sought to determine the prevalence and clinical significance of germline pathogenic/likely pathogenic variants (PV) in patients with advanced lung cancer., Methods: We studied clinical and tumor characteristics of germline PV in 5,118 patients who underwent prospective genomic profiling using paired tumor-normal tissue samples in 468 cancer genes., Results: Germline PV in high/moderate-penetrance genes were observed in 222 (4.3%) patients; of these, 193 patients had PV in DNA damage repair (DDR) pathway genes including BRCA2 (n = 54), CHEK2 (n = 30), and ATM (n = 26) that showed high rate of biallelic inactivation in tumors. BRCA2 heterozygotes with lung adenocarcinoma were more likely to be never smokers and had improved survival compared with noncarriers. Fourteen patients with germline PV in lung cancer predisposing genes (TP53, EGFR, BAP1, and MEN1) were diagnosed at younger age compared with noncarriers, and of tumor suppressors, 75% demonstrated biallelic inactivation in tumors. A significantly higher proportion of germline PV in high/moderate-penetrance genes were detected in high-risk patients who had either a family history of any cancer, multiple primary tumors, or early age at diagnosis compared with unselected patients (10.5% vs. 4.1%; P = 1.7e-04)., Conclusions: These data underscore the biological and clinical importance of germline mutations in highly penetrant DDR genes as a risk factor for lung cancer., Impact: The family members of lung cancer patients harboring PV in cancer predisposing genes should be referred for genetic counseling and may benefit from proactive surveillance., (©2022 American Association for Cancer Research.)

Mammalian models are essential for brain aging research. However, the long lifespan and poor amenability to genetic and pharmacological perturbations have hindered the use of mammals for dissecting aging-regulatory molecular networks and discovering new anti-aging interventions. To circumvent these limitations, we developed an ex vivo model system that faithfully mimics the aging process of the mammalian brain using cultured mouse brain slices. Genome-wide gene expression analyses showed that cultured brain slices spontaneously upregulated senescence-associated genes over time and reproduced many of the transcriptional characteristics of aged brains. Treatment with rapamycin, a classical anti-aging compound, largely abolished the time-dependent transcriptional changes in naturally aged brain slice cultures. Using this model system, we discovered that prions drastically accelerated the development of age-related molecular signatures and the pace of brain aging. We confirmed this finding in mouse models and human victims of Creutzfeldt-Jakob disease. These data establish an innovative, eminently tractable mammalian model of brain aging, and uncover a surprising acceleration of brain aging in prion diseases., (© 2022. The Author(s).)

To realize the goals of precision medicine in complex disease, discriminative clinical risk models are needed. One approach that has been proposed is polygenic risk scores (PRSs). PRSs incorporate information about inherited genetic risk for cancer, specifically those genetic variants that are common in the population. While PRSs are clearly associated with risk of cancer, there is an on-going debate on whether integrating PRSs into clinical practice have utility. Here, we present this important discussion to the cancer clinic. We argue that in cancer, the clinical utility of PRSs will depend on their actionability, or how such a score may guide clinical practice. In turn, the actionability depends on several factors. First, actionability depends on the discriminative power of the score, or how well it predicts who is at risk of the disease. Second, it depends on their comparative performance with respect to existing practice, as a score with good discriminative power will not be useful if there are better predictors used in the clinic. Finally, for a PRS to be useful there must also be available preventive actions. We discuss the strengths and challenges of utilizing a PRS in the context of each of these criteria, and provide insights on what is needed towards moving forward in translating PRSs into the cancer clinic. We further argue that in future studies, beyond predicting cancer risk, similarly developed PRS models may be of utility in predicting prognosis or treatment resistance., Competing Interests: Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-21-698/coif). RJK reports that this work was supported by a grant from the National Institutes of Health (No. R01 CA244948). ZHG reports that this work was supported by a grant from the LUNGevity foundation (No. R33 CA263705). The authors have no other conflicts of interest to declare., (2022 Translational Lung Cancer Research. All rights reserved.)

How we interact with computer graphics has not changed significantly from viewing 2D text and images on a flatscreen since their invention. Yet, recent advances in computing technology, internetworked devices and gaming are driving the design and development of new ideas in other modes of human-computer interfaces (HCIs). Virtual Reality (VR) technology uses computers and HCIs to create the feeling of immersion in a three-dimensional (3D) environment that contains interactive objects with a sense of spatial presence , where objects have a spatial location relative to, and independent of the users. While this virtual environment does not necessarily match the real world, by creating the illusion of reality, it helps users leverage the full range of human sensory capabilities. Similarly, Augmented Reality (AR), superimposes virtual images to the real world. Because humans learn the physical world through a gradual sensory familiarization, these immersive visualizations enable gaining familiarity with biological systems not realizable in the physical world (e.g., allosteric regulatory networks within a protein or biomolecular pathways inside a cell). As VR/AR interfaces are anticipated to be explosive in consumer markets, systems biologists will be more immersed into their world. Here we introduce a brief history of VR/AR, their current roles in systems biology, and advantages and disadvantages in augmenting user abilities. We next argue that in systems biology, VR/AR technologies will be most useful in visually exploring and communicating data; performing virtual experiments; and education/teaching. Finally, we discuss our perspective on future directions for VR/AR in systems biology., Competing Interests: Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

The identification of rare variants associated with schizophrenia has proven challenging due to genetic heterogeneity, which is reduced in founder populations. In samples from the Ashkenazi Jewish population, we report that schizophrenia cases had a greater frequency of novel missense or loss of function (MisLoF) ultra-rare variants (URVs) compared to controls, and the MisLoF URV burden was inversely correlated with polygenic risk scores in cases. Characterizing 141 "case-only" genes (MisLoF URVs in ≥3 cases with none in controls), the cadherin gene set was associated with schizophrenia. We report a recurrent case mutation in PCDHA3 that results in the formation of cytoplasmic aggregates and failure to engage in homophilic interactions on the plasma membrane in cultured cells. Modeling purifying selection, we demonstrate that deleterious URVs are greatly overrepresented in the Ashkenazi population, yielding enhanced power for association studies. Identification of the cadherin/protocadherin family as risk genes helps specify the synaptic abnormalities central to schizophrenia., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) induces tumor cell-specific apoptosis, making it a prime therapeutic candidate. However, many tumor cells are either innately TRAIL-resistant, or they acquire resistance with adaptive mechanisms that remain poorly understood. In this study, we generated acquired TRAIL resistance models using multiple glioblastoma (GBM) cell lines to assess the molecular alterations in the TRAIL-resistant state. We selected TRAIL-resistant cells through chronic and long-term TRAIL exposure and noted that they showed persistent resistance both in vitro and in vivo. Among known TRAIL-sensitizers, proteosome inhibitor Bortezomib, but not HDAC inhibitor MS-275, was effective in overcoming resistance in all cell models. This was partly achieved through upregulating death receptors and pro-apoptotic proteins, and downregulating major anti-apoptotic members, Bcl-2 and Bcl-xL. We showed that CRISPR/Cas9 mediated silencing of DR5 could block Bortezomib-mediated re-sensitization, demonstrating its critical role. While overexpression of Bcl-2 or Bcl-xL was sufficient to confer resistance to TRAIL-sensitive cells, it failed to override Bortezomib-mediated re-sensitization. With RNA sequencing in multiple paired TRAIL-sensitive and TRAIL-resistant cells, we identified major alterations in inflammatory signaling, particularly in the NF-κB pathway. Inhibiting NF-κB substantially sensitized the most resistant cells to TRAIL, however, the sensitization effect was not as great as what was observed with Bortezomib. Together, our findings provide new models of acquired TRAIL resistance, which will provide essential tools to gain further insight into the heterogeneous therapy responses within GBM tumors. Additionally, these findings emphasize the critical importance of combining proteasome inhibitors and pro-apoptotic ligands to overcome acquired resistance.

We investigated a Spanish and Catalan family in which multiple cancer types tracked across three generations, but for which no genetic etiology had been identified. Whole-exome sequencing of germline DNA from multiple affected family members was performed to identify candidate variants to explain this occurrence of familial cancer. We discovered in all cancer-affected family members a single rare heterozygous germline variant (I654V, rs1801201) in ERBB2/HER2 , which is located in a transmembrane glycine zipper motif critical for ERBB2-mediated signaling and in complete linkage disequilibrium ( D ' = 1) with a common polymorphism (I655V, rs1136201) previously reported in some populations as associated with cancer risk. Because multiple cancer types occurred in this family, we tested both the I654V and the I655V variants for association with cancer across multiple tumor types in 6,371 cases of Northern European ancestry drawn from The Cancer Genome Atlas and 6,647 controls, and found that the rare variant (I654V) was significantly associated with an increased risk for cancer (OR = 1.40; P = 0.021; 95% confidence interval (CI), 1.05-1.89). Functional assays performed in HEK 293T cells revealed that both the I655V single mutant (SM) and the I654V;I655V double mutant (DM) stabilized ERBB2 protein and activated ERBB2 signaling, with the DM activating ERBB2 significantly more than the SM alone. Thus, our results suggest a model whereby heritable genetic variation in the transmembrane domain activating ERBB2 signaling is associated with both sporadic and familial cancer risk, with increased ERBB2 stabilization and activation associated with increased cancer risk. PREVENTION RELEVANCE: By performing whole-exome sequencing on germline DNA from multiple cancer-affected individuals belonging to a family in which multiple cancer types track across three generations, we identified and then characterized functional common and rare variation in ERBB2 associated with both sporadic and familial cancer. Our results suggest that heritable variation activating ERBB2 signaling is associated with risk for multiple cancer types, with increases in signaling correlated with increases in risk, and modified by ancestry or family history., (©2021 American Association for Cancer Research.)

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection., Competing Interests: Declaration of Interests E.E.S. serves as chief executive officer for Sema4 and has an equity interest in this company., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Introduction: Lung cancer is the leading cause of cancer deaths in the world, and lung adenocarcinoma (LUAD) is its most prevalent subtype. Symptoms are often found in advanced disease in which treatment options are limited. Identifying genetic risk factors will enable better identification of high-risk individuals., Methods: To identify LUAD risk genes, we performed a case-control association study for gene-level burden of rare, deleterious variants (RDVs) in germline whole-exome sequencing data of 1083 patients with LUAD and 7650 controls, split into discovery and validation cohorts. Of these, we performed whole-exome sequencing on 97 patients and acquired the rest from multiple public databases. We annotated all rare variants for pathogenicity conservatively, using the guidelines of the American College of Medical Genetics and Genomics and ClinVar curation, and investigated gene-level RDV burden using penalized logistic regression. All statistical tests were two-sided., Results: We discovered and replicated the finding that the burden of germline ATM RDVs was significantly higher in patients with LUAD versus controls (combined cohort OR = 4.6; p = 1.7e-04; 95% confidence interval = 2.2-9.5; 1.21% of cases; 0.24% of controls). Germline ATM RDVs were also enriched in an independent clinical cohort of 1594 patients from the MSK-IMPACT study (0.63%). In addition, we observed that an Ashkenazi Jewish (AJ) founder ATM variant, rs56009889, was statistically significantly more frequent in AJ cases versus AJ controls in our cohort (combined AJ cohort OR = 2.7, p = 6.9e-03, 95% confidence interval = 1.3-5.3)., Conclusions: Our results indicate that ATM is a moderate-penetrance LUAD risk gene and that LUAD may be a part of the ATM-related cancer syndrome spectrum. Individuals with ATM RDVs are at an elevated LUAD risk and can benefit from increased surveillance (particularly computed tomography scanning), early detection, and chemoprevention programs, improving prognosis., (Copyright © 2020 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

To better understand the molecular basis of cancer, the NCI's Clinical Proteomics Tumor Analysis Consortium (CPTAC) has been performing comprehensive large-scale proteogenomic characterizations of multiple cancer types. Gene and protein regulatory networks are subsequently being derived based on these proteogenomic profiles, which serve as tools to gain systems-level understanding of the molecular regulatory factories underlying these diseases. On the other hand, it remains a challenge to effectively visualize and navigate the resulting network models, which capture higher order structures in the proteogenomic profiles. There is a pressing need to have a new open community resource tool for intuitive visual exploration, interpretation, and communication of these gene/protein regulatory networks by the cancer research community. In this work, ProNetView-ccRCC (http://ccrcc.cptac-network-view.org/), an interactive web-based network exploration portal for investigating phosphopeptide co-expression network inferred based on the CPTAC clear cell renal cell carcinoma (ccRCC) phosphoproteomics data is introduced. ProNetView-ccRCC enables quick, user-intuitive visual interactions with the ccRCC tumor phosphoprotein co-expression network comprised of 3614 genes, as well as 30 functional pathway-enriched network modules. Users can interact with the network portal and can conveniently query for association between abundance of each phosphopeptide in the network and clinical variables such as tumor grade., (© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK. Immune subtyping revealed a complex landscape, reinforced the association of STK11 with immune-cold behavior, and underscored a potential immunosuppressive role of neutrophil degranulation. Smoking-associated LUADs showed correlation with other environmental exposure signatures and a field effect in NATs. Matched NATs allowed identification of differentially expressed proteins with potential diagnostic and therapeutic utility. This proteogenomics dataset represents a unique public resource for researchers and clinicians seeking to better understand and treat lung adenocarcinomas., Competing Interests: Declaration of Interests B.Z. has received research funding from Bristol-Myers Squibb. All other authors have no conflict of interests to declare., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected millions of people worldwide, igniting an unprecedented effort from the scientific community to understand the biological underpinning of COVID19 pathophysiology. In this Review, we summarize the current state of knowledge of innate and adaptive immune responses elicited by SARS-CoV-2 infection and the immunological pathways that likely contribute to disease severity and death. We also discuss the rationale and clinical outcome of current therapeutic strategies as well as prospective clinical trials to prevent or treat SARS-CoV-2 infection., Competing Interests: Declaration of Interests N.B. serves as an advisor/board member for Neon, Checkpoint Sciences, Primevax, Novartis, Array BioPharma, Roche, Avidea, Boeringer Ingelheim, Rome Therapeutics, Roswell Park, and the Parker Institute for Cancer Immunotherapy. N.B. receives research support from the Parker Insitute, Novocure, Celldex, Genentech, Oncovir, and Regeneron. M.M. serves as an advisor/board member for Celsius, Pionyr, Compugen, Myeloids and Innate pharma and ad hoc for Takeda. M.M. receives research support from Regeneron, Takeda, and Genentech. A.M. has equity in Gilead Sciences and Regeneron Pharmaceuticals., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Distinct prevalence of inherited genetic predisposition may partially explain the difference of cancer risks across ancestries. Ancestry-specific analyses of germline genomes are required to inform cancer genetic risk and prognosis of diverse populations., Methods: We conducted analyses using germline and somatic sequencing data generated by The Cancer Genome Atlas. Collapsing pathogenic and likely pathogenic variants to cancer predisposition genes (CPG), we analyzed the association between CPGs and cancer types within ancestral groups. We also identified the predisposition-associated two-hit events and gene expression effects in tumors., Results: Genetic ancestry analysis classified the cohort of 9899 cancer cases into individuals of primarily European (N = 8184, 82.7%), African (N = 966, 9.8%), East Asian (N = 649, 6.6%), South Asian (N = 48, 0.5%), Native/Latin American (N = 41, 0.4%), and admixed (N = 11, 0.1%) ancestries. In the African ancestry, we discovered a potentially novel association of BRCA2 in lung squamous cell carcinoma (OR = 41.4 [95% CI, 6.1-275.6]; FDR = 0.002) previously identified in Europeans, along with a known association of BRCA2 in ovarian serous cystadenocarcinoma (OR = 8.5 [95% CI, 1.5-47.4]; FDR = 0.045). In the East Asian ancestry, we discovered one previously known association of BRIP1 in stomach adenocarcinoma (OR = 12.8 [95% CI, 1.8-90.8]; FDR = 0.038). Rare variant burden analysis further identified 7 suggestive associations in African ancestry individuals previously described in European ancestry, including SDHB in pheochromocytoma and paraganglioma, ATM in prostate adenocarcinoma, VHL in kidney renal clear cell carcinoma, FH in kidney renal papillary cell carcinoma, and PTEN in uterine corpus endometrial carcinoma. Most predisposing variants were found exclusively in one ancestry in the TCGA and gnomAD datasets. Loss of heterozygosity was identified for 7 out of the 15 African ancestry carriers of predisposing variants. Further, tumors from the SDHB or BRCA2 carriers showed simultaneous allelic-specific expression and low gene expression of their respective affected genes, and FH splice-site variant carriers showed mis-splicing of FH., Conclusions: While several CPGs are shared across patients, many pathogenic variants are found to be ancestry-specific and trigger somatic effects. Studies using larger cohorts of diverse ancestries are required to pinpoint ancestry-specific genetic predisposition and inform genetic screening strategies.

Zika virus (ZIKV) is an emerging, mosquito-borne flavivirus responsible for recent epidemics across the Americas, and it is closely related to dengue virus (DENV). Here, we study samples from 46 DENV-naive and 43 DENV-immune patients with RT-PCR-confirmed ZIKV infection at early-acute, late-acute, and convalescent time points from our pediatric cohort study in Nicaragua. We analyze the samples via RNA sequencing (RNA-seq), CyTOF, and multiplex cytokine/chemokine Luminex to generate a comprehensive, innate immune profile during ZIKV infection. Immunophenotyping and analysis of cytokines/chemokines reveal that CD14 + monocytes play a key role during ZIKV infection. Further, we identify CD169 (Siglec-1) on CD14 + monocytes as a potential biomarker of acute ZIKV infection. Strikingly distinct transcriptomic and immunophenotypic signatures are observed at all three time points. Interestingly, pre-existing dengue immunity has minimal impact on the innate immune response to Zika. Finally, this comprehensive immune profiling and network analysis of ZIKV infection in children serves as a valuable resource., Competing Interests: Declaration of Interest The authors declare no competing interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

K-Ras is the most frequently mutated protein in human cancers. However, until very recently, its oncogenic mutants were viewed as undruggable. To develop inhibitors that directly target oncogenic K-Ras mutants, we need to understand both their mutant-specific and pan-mutant dynamics and conformations. Recently, we have investigated how the most frequently observed K-Ras mutation in cancer patients, G12D, changes its local dynamics and conformations (Vatansever et al., 2019). Here, we extend our analysis to study and compare the local effects of other frequently observed oncogenic mutations, G12C, G12V, G13D and Q61H. For this purpose, we have performed Molecular Dynamics (MD) simulations of each mutant when active (GTP-bound) and inactive (GDP-bound), analyzed their trajectories, and compared how each mutant changes local residue conformations, inter-protein distance distributions, local flexibility and residue pair correlated motions. Our results reveal that in the four active oncogenic mutants we have studied, the α2 helix moves closer to the C-terminal of the α3 helix. However, P-loop mutations cause α3 helix to move away from Loop7, and only G12 mutations change the local conformational state populations of the protein. Furthermore, the motions of coupled residues are mutant-specific: G12 mutations lead to new negative correlations between residue motions, while Q61H destroys them. Overall, our findings on the local conformational states and protein dynamics of oncogenic K-Ras mutants can provide insights for both mutant-selective and pan-mutant targeted inhibition efforts., (© 2020 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

Human epidermal growth factor 2 (HER2) is a ligand-free tyrosine kinase receptor of the HER family that is overexpressed in some of the most aggressive tumours. Although it is known that HER2 dimerization involves a specific region of its extracellular domain, the so-called "dimerization arm", the mechanism of dimerization inhibition remains uncertain. However, uncovering how antibody interactions lead to inhibition of HER2 dimerization is of key importance in understanding its role in tumour progression and therapy. Herein, we employed several computational modelling techniques for a molecular-level understanding of the interactions between HER and specific anti-HER2 antibodies, namely an antigen-binding (Fab) fragment (F0178) and a single-chain variable fragment from Trastuzumab (scFv). Specifically, we investigated the effects of antibody-HER2 interactions on the key residues of "dimerization arm" from molecular dynamics (MD) simulations of unbound HER (in a total of 1 µs), as well as ScFv:HER2 and F0178:HER2 complexes (for a total of 2.5 µs). A deep surface analysis of HER receptor revealed that the binding of specific anti-HER2 antibodies induced conformational changes both in the interfacial residues, which was expected, and in the ECDII (extracellular domain), in particular at the "dimerization arm", which is critical in establishing protein-protein interface (PPI) interactions. Our results support and advance the knowledge on the already described trastuzumab effect on blocking HER2 dimerization through synergistic inhibition and/or steric hindrance. Furthermore, our approach offers a new strategy for fine-tuning target activity through allosteric ligands., Competing Interests: The authors declare no conflict of interest.

To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology., (Copyright © 2019 Elsevier Inc. All rights reserved.)

High mortality rates of glioblastoma (GBM) patients are partly attributed to the invasive behavior of tumor cells that exhibit extensive infiltration into adjacent brain tissue, leading to rapid, inevitable, and therapy-resistant recurrence. In this study, we analyzed transcriptome of motile (dispersive) and non-motile (core) GBM cells using an in vitro spheroid dispersal model and identified SERPINE1 as a modulator of GBM cell dispersal. Genetic or pharmacological inhibition of SERPINE1 reduced spheroid dispersal and cell adhesion by regulating cell-substrate adhesion. We examined TGFβ as a potential upstream regulator of SERPINE1 expression. We also assessed the significance of SERPINE1 in GBM growth and invasion using TCGA glioma datasets and a patient-derived orthotopic GBM model. SERPINE1 expression was associated with poor prognosis and mesenchymal GBM in patients. SERPINE1 knock-down in primary GBM cells suppressed tumor growth and invasiveness in the brain. Together, our results indicate that SERPINE1 is a key player in GBM dispersal and provide insights for future anti-invasive therapy design.

K-Ras is the most frequently mutated oncoprotein in human cancers, and G12D is its most prevalent mutation. To understand how G12D mutation impacts K-Ras function, we need to understand how it alters the regulation of its dynamics. Here, we present local changes in K-Ras structure, conformation and dynamics upon G12D mutation, from long-timescale Molecular Dynamics simulations of active (GTP-bound) and inactive (GDP-bound) forms of wild-type and mutant K-Ras, with an integrated investigation of atomistic-level changes, local conformational shifts and correlated residue motions. Our results reveal that the local changes in K-Ras are specific to bound nucleotide (GTP or GDP), and we provide a structural basis for this. Specifically, we show that G12D mutation causes a shift in the population of local conformational states of K-Ras, especially in Switch-II (SII) and α3-helix regions, in favor of a conformation that is associated with a catalytically impaired state through structural changes; it also causes SII motions to anti-correlate with other regions. This detailed picture of G12D mutation effects on the local dynamic characteristics of both active and inactive protein helps enhance our understanding of local K-Ras dynamics, and can inform studies on the development of direct inhibitors towards the treatment of K-Ras G12D -driven cancers.

Protein kinases are crucial to coordinate cellular decisions and therefore their activities are strictly regulated. Previously we used ancestral reconstruction to determine how CMGC group kinase specificity evolved (Howard et al., 2014). In the present study, we reconstructed ancestral kinases to study the evolution of regulation, from the inferred ancestor of CDKs and MAPKs, to modern ERKs. Kinases switched from high to low autophosphorylation activity at the transition to the inferred ancestor of ERKs 1 and 2. Two synergistic amino acid changes were sufficient to induce this change: shortening of the β3-αC loop and mutation of the gatekeeper residue. Restoring these two mutations to their inferred ancestral state led to a loss of dependence of modern ERKs 1 and 2 on the upstream activating kinase MEK in human cells. Our results shed light on the evolutionary mechanisms that led to the tight regulation of a kinase that is central in development and disease., Competing Interests: DS, SP, RW, MS, HL, JC, BT, ZG, LH No competing interests declared, (© 2019, Sang et al.)

Humans vary considerably both in their baseline and activated immune phenotypes. We developed a user-friendly open-access web portal, ImmuneRegulation, that enables users to interactively explore immune regulatory elements that drive cell-type or cohort-specific gene expression levels. ImmuneRegulation currently provides the largest centrally integrated resource on human transcriptome regulation across whole blood and blood cell types, including (i) ∼43,000 genotyped individuals with associated gene expression data from ∼51,000 experiments, yielding genetic variant-gene expression associations on ∼220 million eQTLs; (ii) 14 million transcription factor (TF)-binding region hits extracted from 1945 ChIP-seq studies; and (iii) the latest GWAS catalog with 67,230 published variant-trait associations. Users can interactively explore associations between queried gene(s) and their regulators (cis-eQTLs, trans-eQTLs or TFs) across multiple cohorts and studies. These regulators may explain genotype-dependent gene expression variations and be critical in selecting the ideal cohorts or cell types for follow-up studies or in developing predictive models. Overall, ImmuneRegulation significantly lowers the barriers between complex immune regulation data and researchers who want rapid, intuitive and high-quality access to the effects of regulatory elements on gene expression in multiple studies to empower investigators in translating these rich data into biological insights and clinical applications, and is freely available at https://immuneregulation.mssm.edu., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Purpose: Lung cancer is the leading cause of cancer deaths worldwide, with substantially better prognosis in early-stage as opposed to late-stage disease. Identifying genetic factors for lung squamous cell carcinoma (SqCC) risk will enable their use in risk stratification, and personalized intensive surveillance, early detection, and prevention strategies for high-risk individuals., Experimental Design: We analyzed whole-exome sequencing datasets of 318 cases and 814 controls (discovery cohort) and then validated our findings in an independent cohort of 444 patients and 3,479 controls (validation cohort), all of European descent. We also combined all the samples from both cohorts, which, after principal component analysis (PCA) and population stratification, included 765 cases and 4,344 controls (combined cohort). We focused on rare, pathogenic variants found in the ClinVar database and used penalized logistic regression to identify genes in which such variants are enriched in cases. All statistical tests were two-sided., Results: We observed an overall enrichment of rare, deleterious germline variants in Fanconi anemia genes in cases with SqCC [joint analysis odds ratio (OR) = 3.08; P = 1.4e-09; 95% confidence interval (CI), 2.2-4.3]. Consistent with previous studies, BRCA2 in particular exhibited an increased overall burden of rare, deleterious variants (joint OR = 3.2; P = 8.7e-08; 95% CI, 2.1-4.7). More importantly, rare, deleterious germline variants were enriched in Fanconi anemia genes even without the BRCA2 rs11571833 variant that is strongly enriched in lung SqCC cases (joint OR = 2.76; P = 7.0e-04; 95% CI, 1.6-4.7)., Conclusions: These findings can be used toward the development of a genetic diagnostic test in the clinic to identify SqCC high-risk individuals, who can benefit from personalized programs, improving prognosis., (©2018 American Association for Cancer Research.)

Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with >88% reconstruction accuracy). It also allows building of a gene regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders., (Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

While increasingly large reference panels for genome-wide imputation have been recently made available, the degree to which imputation accuracy can be enhanced by population-specific reference panels remains an open question. Here, we sequenced at full-depth (≥ 30×), across two platforms (Illumina X Ten and Complete Genomics, Inc.), a moderately large (n = 738) cohort of samples drawn from the Ashkenazi Jewish population. We developed a series of quality control steps to optimize sensitivity, specificity, and comprehensiveness of variant calls in the reference panel, and then tested the accuracy of imputation against target cohorts drawn from the same population. Quality control (QC) thresholds for the Illumina X Ten platform were identified that permitted highly accurate calling of single nucleotide variants across 94% of the genome. QC procedures also identified numerous regions that are poorly mapped using current reference or alternate assemblies. After stringent QC, the population-specific reference panel produced more accurate and comprehensive imputation results relative to publicly available, large cosmopolitan reference panels, especially in the range of rare variants that may be most critical to further progress in mapping of complex phenotypes. The population-specific reference panel also permitted enhanced filtering of clinically irrelevant variants from personal genomes.

Biological networks are becoming increasingly large and complex, pushing the limits of existing 2D tools. iCAVE is an open-source software tool for interactive visual explorations of large and complex networks in 3D, stereoscopic 3D, or immersive 3D. It introduces new 3D network layout algorithms and 3D extensions of popular 2D network layout, clustering, and edge bundling algorithms to assist researchers in understanding the underlying patterns in large, multi-layered, clustered, or complex networks. This protocol aims to guide new users on the basic functions of iCAVE for loading data, laying out networks (single or multi-layered), bundling edges, clustering networks, visualizing clusters, visualizing data attributes, and saving output images or videos. It also provides examples on visualizing networks constrained in physical 3D space (e.g., proteins; neurons; brain). It is accompanied by a new version of iCAVE with an enhanced user interface and highlights new features useful for existing users. © 2018 by John Wiley & Sons, Inc., (© 2018 John Wiley & Sons, Inc.)

We present SpotOn, a web server to identify and classify interfacial residues as Hot-Spots (HS) and Null-Spots (NS). SpotON implements a robust algorithm with a demonstrated accuracy of 0.95 and sensitivity of 0.98 on an independent test set. The predictor was developed using an ensemble machine learning approach with up-sampling of the minor class. It was trained on 53 complexes using various features, based on both protein 3D structure and sequence. The SpotOn web interface is freely available at: http://milou.science.uu.nl/services/SPOTON/ .

Homologous recombination (HR) enables precise DNA repair after DNA double strand breaks (DSBs) using identical sequence templates, whereas homeologous recombination (HeR) uses only partially homologous sequences. Homeologous recombination introduces mutations through gene conversion and genomic deletions through single-strand annealing (SSA). DNA mismatch repair (MMR) inhibits HeR, but the roles of mammalian MMR MutL homologues (MLH1, PMS2 and MLH3) proteins in HeR suppression are poorly characterized. Here, we demonstrate that mouse embryonic fibroblasts (MEFs) carrying Mlh1 , Pms2 , and Mlh3 mutations have higher HeR rates, by using 7,863 uniquely mapping paired direct repeat sequences (DRs) in the mouse genome as endogenous gene conversion and SSA reporters. Additionally, when DSBs are induced by gamma-radiation, Mlh1 , Pms2 and Mlh3 mutant MEFs have higher DR copy number alterations (CNAs), including DR CNA hotspots previously identified in mouse MMR-deficient colorectal cancer (dMMR CRC). Analysis of The Cancer Genome Atlas CRC data revealed that dMMR CRCs have higher genome-wide DR HeR rates than MMR proficient CRCs, and that dMMR CRCs have deletion hotspots in tumor suppressors FHIT/WWOX at chromosomal fragile sites FRA3B and FRA16D (which have elevated DSB rates) flanked by paired homologous DRs and inverted repeats (IR). Overall, these data provide novel insights into the MMR-dependent HeR inhibition mechanism and its role in tumor suppression., Competing Interests: CONFLICTS OF INTEREST No author has a conflicts of interest related to this manuscript to disclose.

K-Ras is the most frequently mutated oncogene in human cancers, but there are still no drugs that directly target it in the clinic. Recent studies utilizing dynamics information show promising results for selectively targeting mutant K-Ras. However, despite extensive characterization, the mechanisms by which K-Ras residue fluctuations transfer allosteric regulatory information remain unknown. Understanding the direction of information flow can provide new mechanistic insights for K-Ras targeting. Here, we present a novel approach -conditional time-delayed correlations (CTC) - using the motions of all residue pairs of a protein to predict directionality in the allosteric regulation of the protein fluctuations. Analyzing nucleotide-dependent intrinsic K-Ras motions with the new approach yields predictions that agree with the literature, showing that GTP-binding stabilizes K-Ras motions and leads to residue correlations with relatively long characteristic decay times. Furthermore, our study is the first to identify driver-follower relationships in correlated motions of K-Ras residue pairs, revealing the direction of information flow during allosteric modulation of its nucleotide-dependent intrinsic activity: active K-Ras Switch-II region motions drive Switch-I region motions, while α-helix-3L7 motions control both. Our results provide novel insights for strategies that directly target mutant K-Ras.

Over 100 genetic loci harbor schizophrenia-associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ∼20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.

Lung cancer etiology is multifactorial, and growing evidence has indicated that long non-coding RNAs (lncRNAs) are important players in lung carcinogenesis. We performed a large-scale meta-analysis of 690,564 SNPs in 15,531 autosomal lncRNAs by using datasets from six previously published genome-wide association studies (GWASs) from the Transdisciplinary Research in Cancer of the Lung (TRICL) consortium in populations of European ancestry. Previously unreported significant SNPs (P value < 1 × 10 -7 ) were further validated in two additional independent lung cancer GWAS datasets from Harvard University and deCODE. In the final meta-analysis of all eight GWAS datasets with 17,153 cases and 239,337 controls, a novel risk SNP rs114020893 in the lncRNA NEXN-AS1 region at 1p31.1 remained statistically significant (odds ratio = 1.17; 95% confidence interval = 1.11-1.24; P = 8.31 × 10 -9 ). In further in silico analysis, rs114020893 was predicted to change the secondary structure of the lncRNA. Our finding indicates that SNP rs114020893 of NEXN-AS1 at 1p31.1 may contribute to lung cancer susceptibility.

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.