Research Fellow resume examples

A. Demonstrated ability to initiate and conduct novel research projects with a strong track record of developing creative solutions to complex problems

• Problem 1: Authors did not know whether Capsaicin-induced gut microbiota changes are the cause for high-fat diet induced obesity
• Solution: Suggested to use antibiotics-induced depletion of gut microbiota and fecal microbiota transplantation using germ free mice
• Problem 2: Authors did not know how to analyze 16S sequence data and present it effectively with clearly written manuscript
• Solution: Conducted 16S data analysis and Predictive Functional Analysis using bioinformatics tools (PAST, XLSTAT, QIIME, PICRUSt, STAMP and Graphpad Prism)/Expressed microbiome data effectively/Drafted final version of manuscript
• Work style: Collaboration
• Accomplishments: Proved the competency in bioinformatics tools for sequence data analysis/Learned microbiota functional analysis using PICRUSt and QIIME tools/Independently executed manuscript writing skills

• Problem 1: Whether omega-3 fatty acids prevent antibiotics-induced gut dysbiosis was not known
• Solution: Confirmed genotype of fat-1 transgenic mice using RT-PCR/Isolated genomic DNA from mice feces/Conducted targeted fecal microbiome analysis using q-PCR
• Problem 2: Microbiome changes during disease progression
• Solution: Collected fecal samples at four time points (Baseline/after antibiotics/after the recovery period/after the western diet)/RT-qPCR analysis of microbiome changes
• Work style:  Independent as well as collaboration with 'BEI Resources', VA, USA
• Accomplishments: Discovered an association between n-3 fatty acids and microbiota-based marker of obesity (FIRMICUTES to BACTEROIDETES ratio) and Colonization Resistance (BIFIDOBACTERIUM to ENTEROBACTERIACEAE ratio)/Selected ‘Study Spotlight' by MGH Research Institute/Selected oral abstract at Harvard Catalyst Child Health Symposium/Manuscript writing skills

• Problem 1: Whether n-6 and n-3 fatty acids exert opposing effects on gut microbiota
• Solution: Confirmed genotype of fat-1 transgenic mice using RT-PCR/Isolated genomic DNA from mice feces/Conducted targeted fecal microbiome analysis using q-PCR/Expressed data as 16S rRNA copies
• Problem 2: Whether gut microbiota mediates the opposing effects of these fatty acids on chronic inflammation
• Solution: Conducted co-housing and antibiotics and fecal microbiota transplantation experiments
• Problem 3: The mechanism for the opposing effects of n-6 and n-3 fatty acids on microbiota
• Solution: RT-PCR/mRNA expression of antimicrobial peptides (AMPs) and gut tight junction proteins (TJPs)
• Work style: Independent as well as collaboration with 'BEI Resources'
• Accomplishments: Established culture dependent and independent microbiological techniques/Learned how to convert genome copies in to 16S rRNA gene copy numbers/Oral abstract and poster presentation at local and international conferences/Clinical trials using type 2 diabetes and cancer patients

• Problem 1: Whether IAP detoxifies bacterially-derived pro-inflammatory molecules to prevent metabolic disease
• Solution: Initiated and conducted 12 different types of novel experiments with WT and IAP knockout mice
• Problem 2: Whether high-fat diet alters expression of endogenous IAP
• Solution: RT-PCR/mRNA analysis of intestinal tissue IAP
• Problem 3: Whether IAP needs gut microbiota to prevent obesity
• Solution: Antibiotics-induced germ free status/Bacterial culture
• Work style: Independent as well as collaboration with Dr. Atul Bhan for fatty liver disease
• Accomplishments: Discovered unique therapy against metabolic syndrome/Studied host-microbiome interactions/Won grants and awards of $350,000 to advance metabolic research/'Posters of Distinction' award at DDW 2012/Publication in one of the world's most-cited multidisciplinary scientific serials with impact factor 10

• Problem: Establishment of Salmonella and Clostridium difficile infection
• Solution: Conducted literature search and established infection-induced colitis models in mice/Bacterial culture
• Work style: Collaboration
• Accomplishments: Played key role identifying novel therapy against infectious colitis/Poster presentation at DDW 2012

• Problem: 16S sequence data analysis and related statistics
• Solution: Conducted generalized linear modeling with a negative binomial distribution/Assisted to use R Software using the vegan
• Accomplishment: Learned R Software​

• Role: RNA extraction and qRT-PCR on terminal ileum/mRNA expression of gut TJPs

• Problem 1: Whether preventing microbiota derived LPS production by oral IAP supplementation could prevent development of alcoholic liver disease
• Solution: Initiated and conducted acute and chronic alcohol consumption experiments in mice.
• Problem 2: Whether IAP affects liver and intestinal mRNA expression of proinflammatory genes
• Solution: Conducted RNA isolation and qRT-PCR/mRNA expression analysis of targeted genes​

• Accomplishments: 'Poster of Excellence award' at Massachusetts General Hospital Research Fellow Poster Celebration, 2017 (one of the 12 posters awarded among the 100 posters)
• Oral presentation 1: Gut Health, Microbiota & Probiotics Throughout the Lifespan: Metabolic & Brain Function, 2016, Harvard Medical School
• Oral presentation 2: 'Elevate your science symposium', Mass General Postdoc Association, Massachusetts General Hospital, 2017
• Oral presentation 3: Inaugural Symposium of the International Society for Omega-3 Research (ISOR), 2017, Massachusetts General Hospital
• Poster presentation: '6th Annual Obesity Research Incubator Session, 2017, The Cardiovascular, Diabetes & Metabolic Disorders(CVDM) Research Center, Brigham & Women's Research Institute, Boston, MA
• Confirmed upcoming oral presentation: Cambridge Healthtech Institute's 3rd Annual 'Targeting the Microbiome' conference, Boston, MA (http://www.discoveryontarget.com/microbiome-therapeutics/)-The Industry's Preeminent Event on Novel Drug Targets

• Problem 1: Whether omega-6 fatty acids induced chronic low-grade inflammation can be derived from gut dysbiosis and metabolic endotoxemia
• Problem 2: Whether omega-6 fatty acids (n-6) induced gut dysbiosis is associated with chronic diseases (obesity, metabolic syndrome and cancer)
• Problem 3: Whether we can prove this with excluding diet related confounding factors
• Solutions: Developed FAT-2 transgenic mouse model and utilized multi-omics technologies to examine the effects of elevated tissue n-6 PUFA on the gut microbiota,metabolic endotoxemia, and chronic low-grade inflammation
• Relevant Techniques: Conducted fecal Nucleic acid isolation and 16S rRNA sequencing,  RT-qPCR and fecal and serum metabolomics
• Bioinformatics tools: Used PAST, XLSTAT, SIMCA-P, QIIME, PICRUSt, STAMP and Graphpad Prism
• Work style: Independent as well as collaboration with Second Genome and Metabolon, Inc companies

​Manuscript 2 (Nature Communications): Ruairi C. Robertson, Kanakaraju Kaliannan, Conall R. Strain, R. Paul Ross, Catherine Stanton, Jing X. Kang. Maternal omega-3 fatty acids regulate offspring obesity through modulation of gut microbiota.

• Problem: Fecal genomic DNA isolation/16S sequence data analysis with effective microbiome data presentation
• Solution: Isolated fecal bacterial DNA/Constructed Library/Conducted bioinformatics using PAST, XLSTAT, SIMCA, QIIME, PICRUSt and Graphpad Prism
• Work style: Collaboration with APC Microbiome Institute, Ireland
• Accomplishments: Mentored pre-doctoral student and technicians/ Demonstrated how to use bioinformatics tools

• Bacterial genomic DNA was extracted from fresh stool samples (~100–180 mg) using the QIAamp DNA Stool Mini Kit 
• In order to increase its effectiveness, the lysis temperature was increased to 95 °C
• The eluted DNA was treated with RNase, concentration was determined by absorbance at 260 nm (A260), and purity was estimated by determining the A260/A280 ratio with a Nanodrop spectrophotometer, diluting to 20 ng/μl 

• qPCR was performed with a PRISM 9000 Light Cycler using the iTaquniversal SYBR Green Supermix and group-specific primers
• The specificity of the primers and the limit of detection were determined  
• Samples and the standards were run in duplicates with a total volume of 20 μ l/well containing 500 nM primer and 40 ng template genomic DNA 
• Amplification programme and data acquisition were performed following the protocol provided with SYBR Green
• A genomic DNA standard from reference strains (BEI Resources, Manassas, VA) was converted into 16S rRNA copy numbers and serially diluted to generate a standard curve
• The 16S rRNA copy number was determined for each genomic standard by first converting the genomic standard quantities into genome copy numbers and then into 16S rRNA copy numbers
• Threshold cycle values from qPCR for the test samples were used to determine their corresponding numbers of 16S rRNA gene copies based on the standard curve
• Data is expressed as 16S rRNA gene copy number per gram of stool as well as relative abundance of percentage of total bacteria at sub-phylum level bacterial groups
• Quantified short chain fatty acids (SCFA) producing fecal bacterial groups (e.g. Lachnospiraceae, Ruminococcaceae and Roseburia) using RT-qPCR
• Quantified SCFA related butyryl-CoA transferase (BCoAT) genes (RT-qPCR)
• Isolated RNA from mice tissues, made cDNA and quantified expression of various genes using RT-qPCR
• Quantified beta-glucuronidase producing bacterial groups using degenerate primers and RT-qPCR

• 16s sequencing library preparation was performed on DNA samples according to the Illumina 16S metagenomic sequencing library protocol in order to generate V3-V4 amplicons
• DNA samples were subjected to an initial PCR reaction utilising primers specific for amplification of the V3-V4 region of the 16S rRNA gene
• Clean-up and purification of the PCR product was performed using the Agencourt AMPure XP system
• Following clean-up and purification, a second PCR reaction was performed in order to incorporate a unique indexing primer pair to each sample
• The PCR products were purified a second time
• Quantification of samples was performed using DNA quantification assay kit
• Following quantification, samples were pooled in equimolar amounts (4nM) and sequenced at Clinical Microbiomics using Illumina MiSeq 2x300 bp paired end sequencing

• The 64-bit version of USEARCH59 and mothur60 is used in combination with several in-house programs for bioinformatic analysis of the sequence data
• Following tag identification and trimming, all sequences from all samples are pooled.
• Paired end reads are merged, truncating reads at a quality score of 4, requiring at least 100 bp overlap and a merged read length between 300 and 600 bp in length
• Sequences with ambiguous bases, without perfect match to the primers, or homopolymer length greater than 8 are discarded and primer sequences trimmed
• Reads are quality filtered, discarding reads with more than 5 expected errors and sequences are strictly dereplicated, discarding clusters smaller than 5
• Sequences are clustered at 97 % sequence similarity, using the most abundant strictly dereplicated reads as centroids and discarding suspected chimeras based on internal comparison
• Additional suspected chimeric OTUs are discarded based on comparison with the Ribosomal Database Project classifier training set v961 using UCHIME62
• Taxonomic assignment of OTUs is done with mothur's PDS version of the RDP training database v14

2. Analysis of 16S sequencing data 

• The Chao1, Abundance-based Coverage Estimator (ACE) and Shannon α-diversity indexes were calculated and rarefaction curve analysis was performed by PAST software.
• Dimensional reduction of the Bray-Curtis distance between microbiome samples using Principal Coordinate Analysis (PCoA) ordination method (PAST and XLSTAT) was done
• Significant differences among groups were tested with Permutational Analysis of Variance (PERMANOVA) using PAST software
• Top 7 taxa which are primarily responsible for an observed difference between groups were identified by SIMPER (Similarity Percentage) method and their contribution to groups (between and within groups) were analyzed using Principal Component variance-covariance type ordination (PAST and XLSTAT software) method
• Differential expression of taxon were identified (nonparametric ANOVA with Benjamini-Hochberg false discovery rate correction; P <0.05) by XLSTAT.
• Relative abundance of taxon were shown by heatmap with hierarchical clustering (HCN) analysis (XLSTAT and Graphpad Prism)
• Microbiota based biomarker discoveries were done with the Linear Discriminant Analysis Effect Size (LEfSe) using online Galaxy server
• LDA scores derived from LEfSe analysis were used to show the relationship between taxon using a cladogram (circular hierarchical tree) of significantly increased or decreased bacterial taxa

• Differential expression of fecal and serum metabolites were identified (nonparametric ANOVA with Benjamini-Hochberg false discovery rate correction; P <0.05) by XLSTAT
• Relative abundance of metabolites were shown by heatmap with hierarchical clustering (HCN) analysis (XLSTAT and Graphpad Prism)
• Biomarker metabolites based on VIP score were identified by applying PCA, PLS-DA and OPLS-DA regression methods on metabolomics data (SIMCA-P software)
• The quality of the models is described by the R2X or R2Y and Q2 values
• To avoid model over-fitting, a default seven-round cross-validation in SIMCA was performed
• The values of R2X, R2Y, and Q2 were used as indicatives to assess the robustness of a pattern recognition model

4. Putative metagenome identification

• Microbial functions were predicted using 16S ribosomal RNA sequencing and phylogenetic reconstruction of unobserved states (PICRUSt) software
• Demultiplexed FASTA sequences were converted in to BIOM format using QIIME software
• The predicted genes and functions were aligned to KEGG database 
• STAMP software was utilized to determine significant putative KEGG orthologs

• MetaPhlAn (Metagenomic Phylogenetic Analysis)
• Computational tool for profiling the composition of microbial communities from metagenomic shotgun sequencing data

• Phylogenetic Investigation of Communities by Reconstruction of Unobserved States
• Predict gene family abundance (e.g. the metagenome) in environmental DNA samples for which only marker gene (e.g. 16S rRNA gene) data are available.

• Annotates and visualizes phylogenetic and taxonomic trees with labels, colors, heatmaps, and other graphical features.

• Linear Discriminant Analysis Effect Size
• An algorithm for High-Dimensional biomarker discovery and explanation
• Identifies genomic features (genes, pathways, or taxa) characterizing the differences between two or more biological conditions (or classes, see figure below)
• It emphasizes both statistical significance and biological relevance
• Allowing researchers to identify differentially abundant features that are also consistent with biologically meaningful categories (subclasses)
• LEfSe first robustly identifies features that are statistically different among biological classes

• Multivariate statistical framework that finds associations between clinical metadata and microbial community abundance or function
• Allows one to detect the effect of a metadata, possibly a phenotype, deconfounding the effects of diet, age, sample origin or any other metadata captured in the study
• Visualization of Co-occurrence networks by CYTOSCAPE software
• Co-inertia analysis (CIA) to assess relationship between genes and metabolites

• Mean differences in Intestinal Alkaline Phosphatase (IAP) levels between Ischemic Heart Disease (IHD) cases and non-IHD  controls were assessed via Analysis of Covariance (ANCOVA) generalized linear regression models 
• The statistical significance of the variance associated with independent variables was assessed from sum of square III using GLM procedure in XLSTAT
• Multiple logistic regressions using ‘multinomial logit regression model' in XLSTAT assessed association between IHD cases with independent risk factors including IAP
• Regression coefficients and odds ratios were used to express the independent risk contribution of IAP to IHD status

D. Knowledgeable and competent in frequently used bioinformatics tools for sequence data analysis

• Principal component analysis (PCA)
• Correspondence Analysis (CA) 
• Multiple Correspondence Analysis (MCA)
• Principal Coordinate Analysis (PCoA)
• Multidimensional Scaling (MDS)
• Factor analysis (FA)
• Discriminant Analysis (DA)
• Agglomerative Hierarchical Clustering (AHC) in Excel
• k-means clustering in Excel tutorial
• Clustering big data sets using k-means then AHC
• Gaussian mixture model clustering
• Filtering observations and variables in PCA charts
• Filtering observations within a PCA
• ANOSIM/PERMANOVA/Mantel test/SIMPER
• Alpha and Beta diversity indices
• Quadrat richness
• Taxonomic distinctness
• Diversity permutation test

2. Modeling data

• Fitting a distribution to a sample of data
• Simple linear regression
• Multiple Linear Regression
• One-way ANOVA & multiple comparisons
• Contrast analysis after a one-way ANOVA
• Two-way unbalanced ANOVA with interactions
• Pairwise multiple comparisons after a multi-way ANOVA
• ANCOVA analysis
• One-way MANOVA
• Logistic regression
• Ordinal logit model
• Multinomial logit model
• Quantile regression
• Cubic spline
• Nonparametric regression (kernel & Lowess)
• Nonlinear regression
• Nonlinear multiple regression
• Partial Least Squares PLS regression
• Partial least squares discriminant analysis (PLSDA)
• Repeated measures ANOVA
• Run repeated measures ANOVA using mixed models
• Random components mixed model
• Two-stage least squares regression

3. Machine Learning

• Classification tree in Excel tutorial
• Association rules for market basket analysis
• K Nearest Neighbors KNN
• Naive Bayes classification
• Training a Support Vector Machine (SVM)

4. OMICS data analysis

• Heat map (OMICS)
• Differential expression (OMICS)

5. Multiblock data analysis

• Run Generalized Procrustes Analysis (GPA)
• Canonical Correspondence Analysis (CCA)
• Canonical Correlation analysis
• Redundancy analysis (RDA)

6. XLSTAT-3DPlot

• 3D plot
• Save a 3D model to reuse it later or on other data

• Organizer and chair of inaugural symposium conducted for International Society for Omega-3 Research (ISOR), January 08, 2017, Massachusetts General Hospital, Boston, MA
• Organized one mini symposium (''Nutrigenomics and Cancer Biology'') for current lab in December 10, 2012
• Strengthened event (''A Showcase of Fighting Spirit against Cancer'') hosted by current lab in May 14, 2013
• Experienced team player, able to work well in a group setting as well as independently in a fast-paced environment

F. Proven ability to work collaboratively

• Dr. Sonoko Narisawa (Sanford Children's Health Research Center, Sanford-Burnham Medical Research Institute, CA)-IAP-knockout mice
• Dr. Elizabeth Hohmann and Dr. H. Shaw Warren (Infectious Disease Unit, Department of Medicine and Pediatrics, Massachusetts General Hospital, Harvard Medical School, Boston, MA)-Infectious colitis and gut dysbiosis
• Dr. Atul K. Bhan (Department of Pathology, Massachusetts General Hospital, Harvard Medical School,Boston, MA)-Fatty liver disease
• Dr. Staton C (APC Microbiome Institute, Biosciences Building, University College Cork, Cork, Ireland)-Omega-3 fatty acids and microbiota
• Dr. Mantian Mi (Center for Nutrition and Food Safety, Institute of Military Preventive Medicine, Third Military Medical University, Chongqing, P.R. China)-Capsaicin and microbiota
• Dr. Anthony Samir (Department of Radiology, Massachusetts General Hospital, USA)-Nonalcoholic fatty liver disease
• Dr. Madhu S. Malo (Atoxin Biotech, Worcester, MA)-Ischemic Heart Disease

• Manuscripts proofreading (e.g. Kang C et al, 2016; PMID: 6)
• Reviewing manuscripts for scientific journals and helping authors to improve their writing skills (e.g. Digestive Disease Sciences journal)
• Passed "Spoken English Proficiency and Interpersonal and Communication Skills'' part of UNITED STATES MEDICAL LICENSING EXAMINATION (USMLE)
• Established successful external collaboration with key microbiome scientists (e.g. Dr. Staton, C from APC Microbiome Institute, Ireland)
• Supervised, trained and mentored visiting scholars and summer interns
• Precisely communicated microbiome discoveries to wide range of audience at local (e.g. Harvard Medical School) and international meetings (e.g. ISOR 2017)

• Digestive Diseases Sciences (DDS)
• Journal of International Medical Research (JIMR)
• Medical Hypothesis
• Nutrition Research
• MNF

Other Editorial Roles

• 'Lead Guest Editor'
• Mediators of Inflammation journal
  

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