Harris Lazaris

Massachusetts Institute of Technology (MIT), Whitehead Institute, Bioinformatics Scientist (Research)

Followers

Following

Public Views

Hello

less

Interests

Uploads

Papers by Harris Lazaris

Shifting gears: Study of immune system parameters of male habitual marathon runners

Frontiers in Immunology

AimMarathon is a running event in which athletes must cover a distance of 42.195 km. In addition ... more AimMarathon is a running event in which athletes must cover a distance of 42.195 km. In addition to participating in marathons, marathoners have incorporated extensive running into their lifestyle. In the present study, we investigated the effect of long-term strenuous exercise in the form of marathon running on the immune system.Methods & ResultsWe collected peripheral blood samples from 37 male marathoners before/after a race and 37 age/sex/body mass index (BMI)-matched healthy sedentary controls. Hematological and biochemical tests revealed race-induced leukocytosis attributable to neutrophilia and significant increases in plasma lactate dehydrogenase (LDH), creatine phosphokinase (CPK), and cortisol concentrations. Phenotypic analysis of lymphocytes revealed race-induced significant decrease in the number of lymphocytes, memory helper T (Th) cells, naive, memory and activated cytotoxic T (Tc) cells, natural killer (NK), NKT, and B1 cells, and a significant increase in the number...

The dynamic clustering of insulin receptor underlies its signaling and is disrupted in insulin resistance

Nature Communications

Insulin receptor (IR) signaling is central to normal metabolic control and is dysregulated in met... more Insulin receptor (IR) signaling is central to normal metabolic control and is dysregulated in metabolic diseases such as type 2 diabetes. We report here that IR is incorporated into dynamic clusters at the plasma membrane, in the cytoplasm and in the nucleus of human hepatocytes and adipocytes. Insulin stimulation promotes further incorporation of IR into these dynamic clusters in insulin-sensitive cells but not in insulin-resistant cells, where both IR accumulation and dynamic behavior are reduced. Treatment of insulin-resistant cells with metformin, a first-line drug used to treat type 2 diabetes, can rescue IR accumulation and the dynamic behavior of these clusters. This rescue is associated with metformin’s role in reducing reactive oxygen species that interfere with normal dynamics. These results indicate that changes in the physico-mechanical features of IR clusters contribute to insulin resistance and have implications for improved therapeutic approaches.

Download

Additional file 9: Figure S6. of HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking

Pairwise overlaps of TAD boundaries. The pairwise overlaps of TAD boundaries are shown for all sa... more Pairwise overlaps of TAD boundaries. The pairwise overlaps of TAD boundaries are shown for all samples of this study, after calling boundaries using hicratio (all reads, d = 0500). Before TAD calling, the Hi-C matrices were either unprocessed (filtered) or corrected using iterative correction (IC) (resolution = 40 kb). (PDF 3847 kb)

Hic-Bench: Initial Release (V0.1)

HiC-bench: a Hi-C analysis pipeline that allows combinatorial parameter exploration... more HiC-bench: a Hi-C analysis pipeline that allows combinatorial parameter exploration and benchmarking HiC-bench is a configurable computational pipeline that allows comprehensive and reproducible analysis of Hi-C sequencing data. It has the following characteristics: It performs complete Hi-C analysis starting with the alignment of reads (fastq files) and ending with the annotation of specific interactions, their visualization and enrichment analysis. It is the first Hi-C pipeline that integrates TAD calling using published methods and our own algorithm. It performs calculation of boundary scores using our own methods and existing ones. Every pipeline step is followed by summary statistics (when applicable) and visualization of the results. This allows quality control and facilitates troubleshooting. It is fully expandable and customizable. Users can follow the included wrapper script template in order to add new tools. It allows parameter exploration and comparison of different methods in a combinatorial fashion. This unique feature facilitates the design and execution of complex benchmark studies that may involve combinations of multiple parameter/tool choices in each step It has been built with reproducibility in mind. All parameter settings are automatically logged.

Additional file 4: Figure S1. of HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking

Hi-C reads filtering statistics. Number (A) and percentage (B) of the various read categories ide... more Hi-C reads filtering statistics. Number (A) and percentage (B) of the various read categories identified during filtering for all datasets used in the study. Mappable reads were over 95% in all samples. Duplicate (ds-duplicate-intra and ds-duplicate-inter; red and pink respectively), non-uniquely mappable (multihit; light blue), single-end mappable (single-sided; dark blue) and unmapped reads (unmapped; dark purple) were discarded. Self-ligation products (ds-same-fragment; orange) and reads mapping too far (ds-too-far; light purple) from restriction sites or too close to one another (ds-too-close; orange) were also discarded. Only double-sided uniquely mappable cis (ds-accepted-intra; dark green) and trans (ds-accepted-inter; light green) read pairs were used for downstream analysis. The x axis represents either the raw read number (A) or the percentage of reads (B) falling within each of the categories described in the legend. The y axis represents the samples. (PDF 1380 kb)

Additional file 8: Figure S5. of HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking

Principal component analysis of boundary scores. Boundary scores were calculated using ratio scor... more

Additional file 11: Figure S8. of HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking

Enrichment of chromatin interactions in human fibroblasts (IMR90) and embryonic stem cells (H1). ... more Enrichment of chromatin interactions in human fibroblasts (IMR90) and embryonic stem cells (H1). The enrichment of certain chromatin marks and CTCF in the top 50,000 chromatin interactions in the IMR90 and H1 samples is shown. Deep blue and larger circle size indicate higher enrichment. (PDF 921 kb)

Additional file 10: Figure S7. of HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking

Visualization of TADs and certain areas of interest. HiC-bench integrates HiCPlotter [23] and it ... more Visualization of TADs and certain areas of interest. HiC-bench integrates HiCPlotter [23] and it offers the ability to easily prepare publication-quality figures. We present the area surrounding NANOG, a gene of particular importance for the maintenance of pluripotency. The Hi-C matrix corresponding to the chr12:3940389â 11948655 genomic region is presented for H1 cells, before and after matrix correction. The matrix is also rotated 45Â° to facilitate TAD visualization. Various boundary scores (intra-max, DI, ratio) are shown as individual tracks along with CTCF binding. The location of NANOG is presented as a blue line. (PDF 1307 kb)

Additional file 7: Figure S4. of HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking

Boundary score calculation. Two adjacent topological domains (red triangles) are depicted. The le... more Boundary score calculation. Two adjacent topological domains (red triangles) are depicted. The left domain (L) is separated from the right domain (R) by a boundary (black circle). The areas of more-frequent intra-domain interactions are in red. The area of less-frequent cross-domain (or inter-domain) interactions is X. We also introduce parameter d which is the maximum distance from the diagonal to be considered for the calculation of boundary scores (default value: dâ =â 2Â Mb). (PDF 1546 kb)

Additional file 5: Figure S2. of HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking

Matrix statistics. Normalized Hi-C counts are presented as a function of the distance between the... more Matrix statistics. Normalized Hi-C counts are presented as a function of the distance between the interacting partners for all samples and correction methods. The Hi-C samples analyzed were GM12878 (light blue), hESCs (H1) (blue), mesenchymal cells (light green), mesendoderm (dark green), neural progenitors (pink), trophectoderm (red), IMR90 (light and dark orange), K562 (light purple), KBM7 (dark purple) and NHEK (yellow). The matrices were either unprocessed (filtered) (top) or corrected using IC (bottom). The y axis represents the normalized count of Hi-C interactions and the x axis the distance between the interacting partners in kilobases. (PDF 2050 kb)

Additional file 2: Table S1. of HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking

HiC-bench task implementation. The table summarizes how the pipeline tasks are implemented, which... more HiC-bench task implementation. The table summarizes how the pipeline tasks are implemented, which are the requirements for their execution and how they are handled by the pipeline-master-explorer script. The first column lists all the tasks performed by the pipeline ranging from alignment to annotation. The second column lists the input directory required for each task while the third one lists the parameter files. Certain tasks depend on the reference genome (human or mouse), thus the genome assembly acts as split variable (column 4). In some tasks, replicates can be grouped using the group variable (column 5). Pairwise comparisons between replicates or samples are also allowed using tuples (column 6). The last column lists the full pipeline-master-explorer command for each pipeline task. (XLSX 10 kb)

Additional file 3: Table S2. of HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking

HiC-bench input-output objects. The table summarizes the inputs and outputs of the TAD-calling ta... more HiC-bench input-output objects. The table summarizes the inputs and outputs of the TAD-calling task using three different methods with parameter values stored in the params files (column 2). The first column describes the tree structure of the input directories that are essentially the different Hi-C matrices for each sample, before (filtered) and after matrix correction using different methods (e.g., IC). The second column lists all the different parameter scripts and the third column corresponds to the tree structure of the generated output objects. (XLSX 10 kb)

Additional file 1: of HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking

HiC-Bench Manual. (PDF 3853 kb)

Additional file 6: Figure S3. of HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking

Pairwise Pearson correlation of Hi-C matrices. Correlograms summarizing all pairwise Pearson corr... more Pairwise Pearson correlation of Hi-C matrices. Correlograms summarizing all pairwise Pearson correlations for all Hi-C samples used in this study: raw (filtered) matrices (left panel) and matrices after iterative correction (right panel). Dark red indicates strong positive correlation and dark blue strong negative. The resolution of the matrices is 40Â kb. (PDF 1405 kb)

FBXW7 modulates cellular stress response and metastatic potential via HSF1

post-translational modification

Tumor immune infiltration estimated from gene expression profiles predicts colorectal cancer relapse

Oncoimmunology, 2021

A substantial fraction of patients with stage I–III colorectal adenocarcinoma (CRC) experience di... more A substantial fraction of patients with stage I–III colorectal adenocarcinoma (CRC) experience disease relapse after surgery with curative intent. However, biomarkers for predicting the likelihood of CRC relapse have not been fully explored. Therefore, we assessed the association between tumor infiltration by a broad array of innate and adaptive immune cell types and CRC relapse risk. We implemented a discovery-validation design including a discovery dataset from Moffitt Cancer Center (MCC; Tampa, FL) and three independent validation datasets: (1) GSE41258 (2) the Molecular Epidemiology of Colorectal Cancer (MECC) study, and (3) GSE39582. Infiltration by 22 immune cell types was inferred from tumor gene expression data, and the association between immune infiltration by each cell type and relapse-free survival was assessed using Cox proportional hazards regression. Within each of the four independent cohorts, CD4+ memory activated T cell (HR: 0.93, 95% CI: 0.90–0.96; FDR = 0.0001) i...

HiC-bench: a computational pipeline for Hi-C analysis, parameter exploration & benchmarking

A Hi-C analysis pipeline which: • Perform... more A Hi-C analysis pipeline which: • Performs all Hi-C analysis steps • Incorporates TAD calling/boundary score calculation • Focuses on reproducibility • Allows combinatorial tool/parameter evaluation The poster was presented at the CSHL "Nuclear Organization &amp; Function" Meeting (2016).

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping

PLOS Biology

RNA-Mediated Feedback Control of Transcriptional Condensates

Cell

Advancing precision medicine in clinical oncology: Whole exome paired tumor-normal DNA and RNA sequencing at a single-institution cancer center

Journal of Clinical Oncology

e14006 Background: Next generation sequencing (NGS) allows for reliable, comprehensive and cost-e... more e14006 Background: Next generation sequencing (NGS) allows for reliable, comprehensive and cost-effective identification of clinically actionable genetic and genomic alterations. The increasing adoption of NGS in clinical oncology has increased our ability to identify germline alterations predisposing to cancer development as well as somatic changes enabling prescription of individualized cancer treatment and enhanced clinical trial participation. Here we summarize implementation of an NGS-based precision medicine initiative involving oncology patients from a single institution cancer center. Methods: IRB-approved NGS matched whole exome (WES) germline and solid tumor somatic tumor sequencing together with somatic tumor RNA sequencing (RNA-seq) were performed using germline DNA extracted from peripheral blood lymphocytes and nucleic acids for tumor DNA and RNA sequencing obtained from formalin-fixed, paraffin-embedded tumor specimens. Results of sequencing and analyses were presente...

Harris Lazaris

Uploads

Papers by Harris Lazaris

Log In