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Abstract To generate new mechanistic hypotheses on the pathogenesis and disease progression of neuroHIV and identify novel therapeutic targets to improve neuropsychological function in people with HIV, we investigated host genes and pathway dysregulations associated with brain HIV RNA load in gene expression profiles of the frontal cortex, basal ganglia, and white matter of HIV+ patients. Pathway analyses showed that host genes correlated with HIV expression in all three brain regions were predominantly related to inflammation, neurodegeneration, and bioenergetics. HIV RNA load directly correlated particularly with inflammation genesets representative of cytokine signaling, and this was more prominent in white matter and the basal ganglia. Increases in interferon signaling were correlated with high brain HIV RNA load in the basal ganglia and the white matter although not in the frontal cortex. Brain HIV RNA load was inversely correlated with genesets that are indicative of neuronal and synaptic genes, particularly in the cortex, indicative of synaptic injury and neurodegeneration. Brain HIV RNA load was inversely correlated with genesets that are representative of oxidative phosphorylation, electron transfer, and the tricarboxylic acid cycle in all three brain regions. Mitochondrial dysfunction has been implicated in the toxicity of some antiretrovirals, and these results indicate that mitochondrial dysfunction is also associated with productive HIV infection. Genes and pathways correlated with brain HIV RNA load suggest potential therapeutic targets to ameliorate neuropsychological functioning in people living with HIV.

Substance use disorder is associated with accelerated disease progression in people with human immunodeficiency virus (HIV; PWH). Problem opioid use, including high-dose opioid therapy, prescription drug misuse, and opioid abuse, is high and increasing in the PWH population. Oxycodone is a broadly prescribed opioid in both the general population and PWH. Here, we allowed HIV transgenic (Tg) rats and wildtype (WT) littermates to intravenously self-administer oxycodone under short-access (ShA) conditions, which led to moderate, stable, “recreational”-like levels of drug intake, or under long-access (LgA) conditions, which led to escalated (dependent) drug intake. HIV Tg rats with histories of oxycodone self-administration under LgA conditions exhibited significant impairment in memory performance in the novel object recognition (NOR) paradigm. RNA-sequencing expression profiling of the medial prefrontal cortex (mPFC) in HIV Tg rats that self-administered oxycodone under ShA conditions exhibited greater transcriptional evidence of inflammation than WT rats that self-administered oxycodone under the same conditions. HIV Tg rats that self-administered oxycodone under LgA conditions exhibited transcriptional evidence of an increase in neuronal injury and neurodegeneration compared with WT rats under the same conditions. Gene expression analysis indicated that glucocorticoid-dependent adaptations contributed to the gene expression effects of oxycodone self-administration. Overall, the present results indicate that a history of opioid intake promotes neuroinflammation and glucocorticoid dysregulation, and excessive opioid intake is associated with neurotoxicity and cognitive impairment in HIV Tg rats.

The pathogenesis and nosology of HIV-associated neurological disease (HAND) remain incompletely understood. Here, to provide new insight into the molecular events leading to neurocognitive impairments (NCI) in HIV infection, we analyzed pathway dysregulations in gene expression profiles of HIV-infected patients with or without NCI and HIV encephalitis (HIVE) and control subjects. The Gene Set Enrichment Analysis (GSEA) algorithm was used for pathway analyses in conjunction with the Molecular Signatures Database collection of canonical pathways (MSigDb). We analyzed pathway dysregulations in gene expression profiles of patients from the National NeuroAIDS Tissue Consortium (NNTC), which consists of samples from 3 different brain regions, including white matter, basal ganglia and frontal cortex of HIV-infected and control patients. While HIVE is characterized by widespread, uncontrolled inflammation and tissue damage, substantial gene expression evidence of induction of interferon (IFN), cytokines and tissue injury is apparent in all brain regions studied, even in the absence of NCI. Various degrees of white matter changes were present in all HIV-infected subjects and were the primary manifestation in patients with NCI in the absence of HIVE. In particular, NCI in patients without HIVE in the NNTC sample is associated with white matter expression of chemokines, cytokines and β-defensins, without significant activation of IFN. Altogether, the results identified distinct pathways differentially regulated over the course of neurological disease in HIV infection and provide a new perspective on the dynamics of pathogenic processes in the course of HIV neurological disease in humans. These results also demonstrate the power of the systems biology analyses and indicate that the establishment of larger human gene expression profile datasets will have the potential to provide novel mechanistic insight into the pathogenesis of neurological disease in HIV infection and identify better therapeutic targets for NCI.

BackgroundMajor advances have been achieved in the characterization of early breast cancer (eBC) genomic profiles. Metastatic breast cancer (mBC) is associated with poor outcomes, yet limited information is available on the genomic profile of this disease. This study aims to decipher mutational profiles of mBC using next-generation sequencing.Methods and findingsWhole-exome sequencing was performed on 216 tumor-blood pairs from mBC patients who underwent a biopsy in the context of the SAFIR01, SAFIR02, SHIVA, or Molecular Screening for Cancer Treatment Optimization (MOSCATO) prospective trials. Mutational profiles from 772 primary breast tumors from The Cancer Genome Atlas (TCGA) were used as a reference for comparing primary and mBC mutational profiles. Twelve genes (TP53, PIK3CA, GATA3, ESR1, MAP3K1, CDH1, AKT1, MAP2K4, RB1, PTEN, CBFB, and CDKN2A) were identified as significantly mutated in mBC (false discovery rate [FDR] < 0.1). Eight genes (ESR1, FSIP2, FRAS1, OSBPL3, EDC4, PALB2, IGFN1, and AGRN) were more frequently mutated in mBC as compared to eBC (FDR < 0.01). ESR1 was identified both as a driver and as a metastatic gene (n = 22, odds ratio = 29, 95% CI [9-155], p = 1.2e-12) and also presented with focal amplification (n = 9) for a total of 31 mBCs with either ESR1 mutation or amplification, including 27 hormone receptor positive (HR+) and HER2 negative (HER2-) mBCs (19%). HR+/HER2- mBC presented a high prevalence of mutations on genes located on the mechanistic target of rapamycin (mTOR) pathway (TSC1 and TSC2) as compared to HR+/HER2- eBC (respectively 6% and 0.7%, p = 0.0004). Other actionable genes were more frequently mutated in HR+ mBC, including ERBB4 (n = 8), NOTCH3 (n = 7), and ALK (n = 7). Analysis of mutational signatures revealed a significant increase in APOBEC-mediated mutagenesis in HR+/HER2- metastatic tumors as compared to primary TCGA samples (p < 2e-16). The main limitations of this study include the absence of bone metastases and the size of the cohort, which might not have allowed the identification of rare mutations and their effect on survival.ConclusionsThis work reports the results of the analysis of the first large-scale study on mutation profiles of mBC. This study revealed genomic alterations and mutational signatures involved in the resistance to therapies, including actionable mutations.

Stevens-Johnson syndrome (SJS) and Toxic Epidermal Necrolysis (TEN) represent rare but serious adverse drug reactions (ADRs). Both are characterized by distinctive blistering lesions and significant mortality rates. While there is evidence for strong drug-specific genetic predisposition related to HLA alleles, recent genome wide association studies (GWAS) on European and Asian populations have failed to identify genetic susceptibility alleles that are common across multiple drugs. We hypothesize that this is a consequence of the low to moderate effect size of individual genetic risk factors. To test this hypothesis we developed Pointer, a new algorithm that assesses the aggregate effect of multiple low risk variants on a pathway using a gene set enrichment approach. A key advantage of our method is the capability to associate SNPs with genes by exploiting physical proximity as well as by using expression quantitative trait loci (eQTLs) that capture information about both cis- and trans-acting regulatory effects. We control for known bias-inducing aspects of enrichment based analyses, such as: 1) gene length, 2) gene set size, 3) presence of biologically related genes within the same linkage disequilibrium (LD) region, and, 4) genes shared among multiple gene sets. We applied this approach to publicly available SJS/TEN genome-wide genotype data and identified the ABC transporter and Proteasome pathways as potentially implicated in the genetic susceptibility of non-drug-specific SJS/TEN. We demonstrated that the innovative SNP-to-gene mapping phase of the method was essential in detecting the significant enrichment for those pathways. Analysis of an independent gene expression dataset provides supportive functional evidence for the involvement of Proteasome pathways in SJS/TEN cutaneous lesions. These results suggest that Pointer provides a useful framework for the integrative analysis of pharmacogenetic GWAS data, by increasing the power to detect aggregate effects of multiple low risk variants. The software is available for download at https://sourceforge.net/projects/pointergsa/.

Alzheimer's disease (AD) is a complex multifactorial disorder with poorly characterized pathogenesis. Our understanding of this disease would thus benefit from an approach that addresses this complexity by elucidating the regulatory networks that are dysregulated in the neural compartment of AD patients, across distinct brain regions. Here, we use a Systems Biology (SB) approach, which has been highly successful in the dissection of cancer related phenotypes, to reverse engineer the transcriptional regulation layer of human neuronal cells and interrogate it to infer candidate Master Regulators (MRs) responsible for disease progression. Analysis of gene expression profiles from laser-captured neurons from AD and controls subjects, using the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe), yielded an interactome consisting of 488,353 transcription-factor/target interactions. Interrogation of this interactome, using the Master Regulator INference algorithm (MARINa), identified an unbiased set of candidate MRs causally responsible for regulating the transcriptional signature of AD progression. Experimental assays in autopsy-derived human brain tissue showed that three of the top candidate MRs (YY1, p300 and ZMYM3) are indeed biochemically and histopathologically dysregulated in AD brains compared to controls. Our results additionally implicate p53 and loss of acetylation homeostasis in the neurodegenerative process. This study suggests that an integrative, SB approach can be applied to AD and other neurodegenerative diseases, and provide significant novel insight on the disease progression.

Mapping the chromosomal locations of transcription factors, nucleosomes, histone modifications, chromatin remodeling enzymes, chaperones, and polymerases is one of the key tasks of modern biology, as evidenced by the Encyclopedia of DNA Elements (ENCODE) Project. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. Mapping such protein-DNA interactions in vivo using ChIP-seq presents multiple challenges not only in sample preparation and sequencing but also for computational analysis. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. We address all the major steps in the analysis of ChIP-seq data: sequencing depth selection, quality checking, mapping, data normalization, assessment of reproducibility, peak calling, differential binding analysis, controlling the false discovery rate, peak annotation, visualization, and motif analysis. At each step in our guidelines we discuss some of the software tools most frequently used. We also highlight the challenges and problems associated with each step in ChIP-seq data analysis. We present a concise workflow for the analysis of ChIP-seq data in Figure 1 that complements and expands on the recommendations of the ENCODE and modENCODE projects. Each step in the workflow is described in detail in the following sections.

Assembly of a transcriptional and post‐translational molecular interaction network in B cells, the human B‐cell interactome (HBCI), reveals a hierarchical, transcriptional control module, where MYB and FOXM1 act as synergistic master regulators of proliferation in the germinal center (GC). Eighty percent of genes jointly regulated by these transcription factors are activated in the GC, including those encoding proteins in a complex regulating DNA pre‐replication, replication, and mitosis. These results indicate that the HBCI analysis can be used for the identification of determinants of major human cell phenotypes and provides a paradigm of general applicability to normal and pathologic tissues.

BackgroundThe Mixed Lymphocyte Reaction (MLR) consists in the allogeneic co-culture of monocytes derived dendritic cells (MoDCs) with T cells from another donor. This in vitro assay is largely used for the assessment of immunotherapy compounds. Nevertheless, the phenotypic changes associated with lymphocyte responsiveness under MLR have never been thoroughly evaluated.MethodsHere, we used multiplex cytokine and chemokine assays, multiparametric flow cytometry and single cell RNA sequencing to deeply characterize T cells activation and function in the context of CD4+- and CD8+-specific MLR kinetics.ResultsWe showed that CD4+ and CD8+ T cells in MLR share common classical markers of response such as polyfunctionality, increased proliferation and CD25 expression but differ in their kinetics and amplitude of activation as well as their patterns of cytokines secretion and immune checkpoints expression. The analysis of immunoreactive Ki-67+CD25+ T cells identified PBK, LRR1 and MYO1G as new potential markers of MLR response. Using cell-cell communication network inference and pathway analysis on single cell RNA sequencing data, we also highlighted key components of the immunological synapse occurring between T cells and the stimulatory MoDCs together with downstream signaling pathways involved in CD4+ and CD8+ T cells activation.ConclusionThese results provide a deep understanding of the kinetics of the MLR assay for CD4+ or CD8+ T cells and may allow to better characterize compounds impacting MLR and eventually identify new strategies for immunotherapy in cancer.

This article examines the process of including people deemed to have intellectual disabilities in the dissemination of knowledge through academic journals. It takes a self-ethnographic look at the experience of two researchers co-writing a scientific paper with two individuals deemed to have intellectual disabilities. After presenting current knowledge on research participation with a focus on issues of knowledge and power-sharing with people deemed to have intellectual disabilities, the authors explore the stages of writing, dissemination, and authorship in inclusive research practices.

Introducción. El interferón gama (IFNγ) es la citocina más potente para controlar la infección por Mycobacterium tuberculosis, el agente etiológico de la tuberculosis humana. Los pacientes con tuberculosis activa presentan reducción de los niveles de IFNγ, lo cual parece explicar la inmunidad poco efectiva contra el bacilo. La disminución de su expresión o alteraciones funcionales de los factores transactivadores del promotor del gen de IFNγ, podrían explicar la reducción de los niveles de IFNγ enlos pacientes con tuberculosis. Objetivo. Determinar la asociación de variantes genéticas en los factores de transcripción TBET,STAT1, STAT4 y HLX con sensibilidad o resistencia a tuberculosis pulmonar. Materiales y métodos. Se seleccionaron ocho polimorfismos de un solo nucleótido (Single-NucleotidePolymorphism, SNP) y se estableció su genotipo, en 466 pacientes con tuberculosis pulmonar y 300 controles sanos en Colombia; además, se hizo un análisis de asociación alélica y genética. Resultados. Los resultados indican que los SNP de los factores de transcripción estudiados no están asociados con tuberculosis; sin embargo, el polimorfismo rs11650354 en TBET puede estar implicado en la disminución de riesgo de tuberculosis. El genotipo TT de TBET se asoció significativamente con protección contra tuberculosis usando un modelo genético recesivo (OR=0,089; CI95%: 0,01-0,73;p=0,0069); sin embargo, la corrección mediante pruebas múltiples de ajuste abolió esta asociación (Empirical P Value, EMP2=0,61). Conclusión. En este estudio se sugiere un efecto de la variante rs11650354 de TBET sobre la resistencia a la tuberculosis en la población colombiana. Es necesario desarrollar un estudio de replicación usando muestras adicionales para confirmar esta asociación sugestiva. doi: http://dx.doi.org/10.7705/biomedica.v33i2.790

Motivation: An increasingly common application of gene expression profile data is the reverse engineering of cellular networks. However, common procedures to normalize expression profiles generated using the Affymetrix GeneChips technology were originally developed for a rather different purpose, namely the accurate measure of differential gene expression between two or more phenotypes. As a result, current evaluation strategies lack comprehensive metrics to assess the suitability of available normalization procedures for reverse engineering and, in general, for measuring correlation between the expression profiles of a gene pair. Results: We benchmark four commonly used normalization procedures (MAS5, RMA, GCRMA and Li-Wong) in the context of established algorithms for the reverse engineering of protein–protein and protein–DNA interactions. Replicate sample, randomized and human B-cell data sets are used as an input. Surprisingly, our study suggests that MAS5 provides the most faithful cellular network reconstruction. Furthermore, we identify a crucial step in GCRMA responsible for introducing severe artifacts in the data leading to a systematic overestimate of pairwise correlation. This has key implications not only for reverse engineering but also for other methods, such as hierarchical clustering, relying on accurate measurements of pairwise expression profile correlation. We propose an alternative implementation to eliminate such side effect. Contect: califano@c2b2.columbia.edu [ABSTRACT FROM AUTHOR]