Daily Archives: December 14, 2023

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Cas9-induced targeted integration of large DNA payloads in primary human T cells via homology-mediated end-joining ... - Nature.com

Ethics statement

Human Study: The lung specimens from the COVID-19 positive human subjects were collected using autopsy (study was IRB Exempt). All donations to this trial were obtained after telephone consent followed by written email confirmation with next of kin/power of attorney per California state law (no in-person visitation could be allowed into the COVID-19 ICU during the pandemic). The detailed patient characteristics were published elsewhere (PMID: 34127431). For normal lung tissues, lung biopsies were obtained after surgical resection of lungs by cardiothoracic surgeons as before [https://elifesciences.org/articles/66417]. Deidentified lung tissues obtained during surgical resection, which were deemed excess by clinical pathologists, were collected using an approved human research protocol (IRB no. 101590). Blood samples were obtained from UTMB Biorepository of research subjects with a laboratory diagnosis of COVID-19 that consented to participate in the Clinical Characterization Protocol for Severe Emerging Infections (UNMC IRB no. 146-20-FB/UTMB IRB no. 20-0066). The normal healthy subjects blood cell pellets were obtained under UTMB IRB no. 14-0131 and 20-0097.

Animal (Hamster) study: Lung samples from 8-week-old male Syrian hamsters were generated from experiments conducted exactly as in previously published studies (PMID: 32540903). Animal studies were approved and performed in accordance with Scripps Research IACUC Protocol no. 20-0003 and UTMB IACUC Protocol no. 2005060.

Publicly available COVID-19 gene expression databases were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus website (GEO)69,70,71. If the dataset was not normalized, RMA (Robust Multichip Average)72,73 was used for microarrays and TPM (Transcripts Per Millions)74,75 was used for RNASeq data for normalization. We used log2 (TPM+1) to compute the final log-reduced expression values for RNASeq data. Accession numbers for these crowd-sourced datasets are provided in the figures and manuscript. Single Cell RNASeq data from GSE145926 was downloaded from GEO in the HDF5 Feature Barcode Matrix Format. The filtered barcode data matrix was processed using Seurat v3 R package76. Pseudo bulk analysis of GSE145926 dataset was performed by adding counts from the different cell subtypes and normalized using log2 (CPM+1). All of the above datasets were processed using the Hegemon data analysis framework77,78,79.

Time (duration in hospital) and status (whether the patient is discharged from hospital) were derived from the hospital-free days post 45-day follow-up from COVID-19 patients (n=100, GSE157103). All non-COVID-19 patients (n=26, GSE157103) were excluded from the analysis. KaplanMeier (KM) analysis is performed using lifelines python package version 0.14.6. All KM analyses use the StepMiner threshold+0.5 noise margin as the threshold to separate the patients into high and low groups.

COVID-19 samples were inactivated by storing in 10 % formalin for 2 days and then were transferred to zinc-formalin solution for another 3 days. The decontaminated tissues were transferred to 70% ethanol and cassettes were prepared for tissue sectioning. The slides containing hamster and human lung tissue sections were de-paraffinized in xylene (Sigma-Aldrich, catalog no. 534056) and rehydrated in graded alcohols to water. For NEIL2 antigen retrieval, slides were immersed in Tris-EDTA buffer (pH 9.0) and boiled for 10min at 100C inside a pressure cooker. Endogenous peroxidase activity was blocked by incubation with 3% H2O2 for 10min. To block non-specific protein binding 2.5% goat serum (Vector Laboratories, catalog no. MP-7401) was added. Tissues were then incubated with rabbit anti-NEIL2 polyclonal antibody (in house generated, 33) for 1.5h at room temperature in a humidified chamber and then rinsed with TBS or PBS 3x, 5min each. Sections were incubated with horse anti-rabbit IgG (Vector Laboratories, catalog no. MP-7401) secondary antibodies for 30min at room temperature and then washed with TBS or PBS 3x, 5min each; incubated with 3,3-diaminobenzidine tetrahydrochloride (DAB) (Thermo Scientific, catalog no. 34002), counterstained with hematoxylin (Sigma-Aldrich, catalog no. MHS1) for 30s, dehydrated in graded alcohols, cleared in xylene, and cover slipped. Epithelial and stromal components of the lung tissue were identified by staining duplicate slides in parallel with hematoxylin and eosin (Sigma-Aldrich, catalog no. E4009) and visualizing by Leica DM1000 LED (Leica Microsystems, Germany).

IHC images were randomly sampled at different 300300 pixel regions of interest (ROI). The ROIs were analyzed using IHC Profiler80. IHC Profiler uses a spectral deconvolution method of DAB/hematoxylin color spectra by using optimized optical density vectors of the color deconvolution plugin for proper separation of the DAB color spectra. The histogram of the DAB intensity was divided into 4 zones: high positive (060), positive (61120), low positive (121180) and negative (181235). High positive, positive, and low positive percentages were combined to compute the final percentage positive for each ROI. The range of values for the percent positive is compared among different experimental groups.

Lung specimens from COVID-19 positive human subjects were collected using autopsy procedures at the University of California San Diego (the study was IRB Exempt) following guidelines from the Centers for Disease Control and Prevention (CDC) and College of American Pathologists autopsy committee. All donations to this trial were obtained after telephone consent followed by written email confirmation with next of kin/power of attorney per California state law (no in-person visitation could be allowed into the COVID-19 ICU during the pandemic). (https://www.cdc.gov/coronavirus/2019-ncov/hcp/guidance-postmortem-specimens.html and https://documents.cap.org/documents/COVID-Autopsy-Statement-05may2020.pdf). Lung specimens were collected in 10 % Zinc-formalin and stored for 72h before processing for histology as done previously81,82.

Blood cell pellets stored in TRIzol LS Reagent (Invitrogen, catalog no. 10296010) were obtained from the UTMB Biorepository for Severe Emerging Infections from research subjects with a laboratory diagnosis of COVID-19 that consented to participate in the Clinical Characterization Protocol for Severe Emerging Infections (UNMC IRB no. 146-20-FB/UTMB IRB no. 20-0066). Samples were used from subjects categorized as having moderate or severe COVID-19 based on the following criteria: moderate disease if requiring oxygen via nasal cannula, severe disease if requiring oxygen via non-invasive ventilation (e.g., CPAP, BiPAP, High-Flow nasal cannula, venturi mask). The normal healthy subjects blood cell pellets were obtained in TRIzol LS Reagent under UTMB IRB # 14-0131 and 20-0097. Total RNA was isolated as per manufacturers protocol and subjected to real time reverse transcriptase-quantitative Polymerase Chain Reaction.

Total RNA extraction was performed from cells using TRIzol Reagent (Invitrogen, catalog no. 15596026) or TRIzol LS Reagent. Total RNA (up to 2g) was used to synthesize cDNA with a PrimeScriptTM RT Kit with gDNA Eraser (TaKaRa, catalog no. RR047A) and qPCR was carried out using TB Green Premix Ex Taq II (Tli RNase H Plus; TaKaRa, catalog no. RR820A) in Applied Biosystems 7500 Real-Time PCR Systems with thermal cycling conditions of 94C for 5min, (94C for 10s, and 60C for 1min) for 40 cycles, and 60C for 5min. The target mRNA levels were normalized to that of GAPDH or 18S RNA. Primer sequences used in the assay are listed in Supplementary Table1. In each case, DNase-treated RNA samples without reverse transcriptase were amplified to test genomic DNA contamination.

Syrian golden hamsters (Hamster/Golden Syrian Hamster/Male/8 weeks old/Charles River/Strain Code 049) experiments were approved by the Scripps Research Institute Institutional Animal Care and Use Committee/Protocol 20-0003, and were carried out in accordance with recommendations. Lung samples were collected from 8-week-old Golden Syrian hamsters post SARS-CoV-2 infection conducted exactly as in a previously published study38. Briefly, lungs from hamsters challenged with SARS-CoV-2 (1106 PFU) were harvested on day 5 (peak weight loss) and NEIL2 protein and mRNA levels were analyzed by IHC and RT-qPCR, respectively. Syrian golden hamsters (Male/8 weeks old) were infected with SARS-CoV2 as approved by the UTMB IACUC (protocol no. 2005060) and nuclear extract was prepared from the uninfected and infected hamster lungs at 10 days post infection as described before33,39, and DNA was extracted from the same samples for LA-qPCR.

A549 cells stably expressing human angiotensin I converting enzyme 2 (A549-ACE2)83 is maintained in Eagles Minimum Essential Media (EMEM; Gibco, Cat # 11095080), containing 10% fetal bovine serum (FBS), 100units/ml penicillin and 100g/mL streptomycin. A549-ACE2 cells grown in six-well plates at ~70% confluence were transduced with recombinant proteins using Pierce Protein Transfection Reagent according to manufacturers recommendations (Pierce, Thermo Scientific, catalog no. 89850). In brief, Pierce reagent (dissolved in 250L of methanol or chloroform) was evaporated to remove traces of solvent and 2g of rNEIL1, or rNEIL2 protein was added in PBS, vortexed, incubated for 5min at room temperature, then the mixture was supplemented with serum free medium. Mixtures were added directly onto the cell monolayers, incubated for 4h in a 5% CO2 containing incubator at 37C and then one volume of 20% serum-containing medium was added for overnight. Transfection efficiency varied between 68 and 75% as determined in parallel experiments by indirect immunofluorescence assays using anti-NEIL2 or anti-NEIL1 (in house generated84) antibodies. Transduced A549/ACE2 cells were infected with SARS-CoV-2 at MOI 11.87. After incubation for an hour with viral inoculum, cells were washed three times with EMEM. Infected cells were harvested at indicated time points in various lysis buffers, depending on the downstream experiment. Supernatants from infected cells were harvested at 24h post-infection for measuring the infectious virus titers by the TCID50 assay using Vero E6 cells. Briefly, 50L supernatants from infected cells were serially diluted (10-fold) in EMEM supplemented with 2% FBS; 100L of serially diluted samples were added to Vero E6 cells grown in 96-well plates and cultivated at 37C for 3 days followed by observation under a microscope for the status of virus-induced formation of cytopathic effect (CPE) in individual wells. The titers were expressed as log TCID50/mL.

Human bronchial epithelium cell line, BEAS-2B (ATCC CRL-9609) stably expressing NEIL2-FLAG,human gastric adenocarcinoma (AGS, ATCC CRL-1739) and human embryonic kidney cells (HEK29385) were grown at 37C and 5% CO2 in DMEM/F-12 (1:1) containing 10% FBS, 100units/ml penicillin and 100units/ml streptomycin. For all experiments, 5060% confluent cells were used. We routinely tested cell lines for mycoplasma contaminations using the PCR-based Venor GeM Mycoplasma Detection Kit (Sigma, catalog no. MP0025). Control or stable BEAS-2B cells at ~70% confluency were transiently transfected with vector expressing GFP with (SARS-CoV2-5-UTR-eGFP construct, synthesized and cloned by GenScript Inc.) or without (UTR-Less-eGFP construct) UTR (100ng) using Lipofectamine TM 2000 (Invitrogen, catalog no. 11668027), according to the suppliers protocol. To monitor transfection efficiency, a reporter gene construct (0.25g) containing -galactosidase downstream to the SV40 promoter was co-transfected. Cells were allowed to recover for 16h in media with serum and then GFP florescence was measured using an ECHO florescent microscope (ECHO Revolve-R4). Total RNA and DNA were isolated for subsequent qPCR analysis.

The proteins in the nuclear extracts (from Hamster lungs)/whole cell extracts A549-ACE2 cells were separated onto a Bio-Rad 420% gradient Bis-Tris gel, then electro-transferred on a nitrocellulose (0.45m pore size; GE Healthcare) membrane using 1X Bio-Rad transfer buffer. The membranes were blocked with 5% w/v skimmed milk in TBST buffer (1X Tris-Buffered Saline, 0.1% Tween 20) and immunoblotted with appropriate antibodies SARS-CoV-2 spike protein (S1-NTD) (Cell Signaling Technology, catalog no. 56996S), GAPDH (BioBharati Life Sciences, catalog no. AB0060), Histidine (BioBharati Life Sciences, catalog no. AB0010), NEIL233, OGG1 (in-house generated86), NEIL1 and APE1 (in-house generated87), and HDAC2 (Histone deacetylase 2, GeneTex, catalog no. GTX109642). The membranes were extensively washed with 1% TBST followed by incubation with anti-isotype secondary antibody (Cell Signaling Technology, catalog no. 7074) conjugated with horseradish peroxidase in 5% skimmed milk at room temperature. Subsequently, the membranes were further washed three times (10min each) in 1% TBST, developed and imaged using kwikquant image analyzer and image analysis software (ver. 5.2) (Kindle Biosciences). Due to cross reactivity of common secondary antibody with the pre developed membrane, the samples were run in parallel gels in similar conditions, and developed with different antibodies. For all the primary antibodies, 1:1000 dilution was used and for secondary antibody, 1:2000 dilution was used.

RNA-ChIP assays were performed as described earlier39. Briefly, cells were cross-linked in 1% formaldehyde for 10min at room temperature. Then 125mM Glycine was added for 5min at room temperature to stop crosslinking and then samples were centrifuged at 1000g at 4C for 5min to pellet the cells. The cell pellet was re-suspended in sonication buffer, containing 50mM Tris-HCl pH 8.0, 10mM EDTA and 1% SDS with 1X Protease inhibitor cocktail and sonicated to an average DNA size of ~300bp using a sonicator (Qsonica Sonicators). The supernatants were diluted with 15mM Tris-HCl pH 8.0, 1.0mM EDTA, 150mM NaCl, 1% Triton X-100, 0.01% SDS containing protease inhibitors, and incubated with anti-NEIL1, -NEIL2, -FLAG (Millipore, catalog no. F1804) or normal IgG (Santa Cruz, catalog no. sc-2025) antibodies overnight at 4C. Immunocomplexes (ICs) were captured by Protein A/G PLUS agarose beads (Santa Cruz, catalog no. sc-2003), that were then washed sequentially in buffer I (20mM Tris-HCl pH 8.0, 150mM NaCl, 1mM EDTA, 1% Triton-X-100 and 0.1% SDS); buffer II (same as buffer I, except containing 500mM NaCl); buffer III (1% NP-40, 1% sodium deoxycholate, 10mM Tris-HCl pH 8.0, 1mM EDTA), and finally with 1X Tris-EDTA (pH 8.0) buffer at 4C for 5min each. RNase inhibitor (50Uml1, Roche, catalog no. 03335402001) was added to sonication and IP buffers, and 40Uml1 to each wash buffer. The ICs were extracted from the beads with elution buffer (1% SDS and 100mM NaHCO3) and de-crosslinked for 2h at 65C. RNA isolation was carried out in acidic phenolchloroform followed by ethanol precipitation with GlycoBlue (Life Technologies, catalog no. AM9516) as a carrier. Reverse transcription and cDNA preparation was performed using a PrimeScript RT Kit with gDNA Eraser. RNA-ChIP samples were analyzed by qPCR using specific primers (listed in Supplementary Table1) and represented as percentage input after normalization to IgG.

Wild-type recombinant His-tagged -NEIL2, -NEIL2-ZnF mutant (ZnF-NEIL2mut) and -NEIL1 proteins were purified from E. coli using protocol as described earlier64. Briefly, pET22b (Novagen) vector containing C-terminal 6xHis tagged Coding DNA Sequence (CDS) of various proteins was transformed into E. coli BL21(DE3) RIPL Codon-plus cells (Agilent technologies, catalog no. 230280). The log-phase culture (A600=0.40.6) of E. coli was induced with 0.5mM isopropyl-1-thio--D-galactopyranoside (IPTG) and grown at 16C for 16h. After centrifugation, the cell pellets were suspended in a lysis buffer (Buffer A) containing 25mM Tris-HCl, pH 7.5, 500mM NaCl, 10% glycerol, 1mM -mercaptoethanol (-ME), 0.25% Tween 20, 5mM imidazole, 2mM phenylmethylsulfonyl fluoride (PMSF). After sonication, the lysates were spun down at 13,000rpm and the supernatant was loaded onto HisPur Cobalt Superflow Agarose (Thermo Scientific, catalog no. 25228), previously equilibrated with Buffer A, and incubated for 2h at 4C. After washing with Buffer A with a gradient of increasing concentration of imidazole (10, 20, 30, 40mM), the His-tagged proteins were eluted with an imidazole gradient (80500mM imidazole in buffer containing 25mM Tris-HCl, pH-7.5, 300mM NaCl, 10% glycerol, 1mM -ME, 0.25% Tween 20). After elution, the peak protein fractions (in the range of 100250mM imidazole) were dialyzed against Buffer C (1X PBS, pH 7.5, 1mM dithiothreitol (DTT), and 25% glycerol) and stored at 20C in aliquots.

The Corona virus nsp12 (GenBank: MN908947) gene, cloned into a modified pET24b vector, with the C-terminus possessing a 10 His-tag, was a gift from Dr. Whitney Yin. The plasmid was transformed into E. coli BL21 (DE3) RIPL Codon-plus cells, and the transformed cells were cultured at 37C in LB media containing 100mg/L ampicillin. After the OD600 reached 0.8, the culture was cooled to 16C and supplemented with 0.5mM IPTG. After overnight induction, the cells were harvested through centrifugation, and the pellets were re-suspended in lysis buffer (20mM Tris-HCl, pH 8.0, 150mM NaCl, 4mM MgCl2, 10% glycerol). The rest of the procedure is same as above with following modifications: the His-tagged protein was eluted with an imidazole gradient (80250mM imidazole in buffer containing 20mM Tris-HCl, pH 8.0, 150mM NaCl, 4mM MgCl2, 10% glycerol). Similarly, nsp7 and nsp8 genes, individually cloned in pET22b and pET30a+ vectors, respectively, were expressed in E. coli as described in case of NEIL proteins. After elution, the peak protein fractions of these proteins were dialyzed against Buffer D (20mM Tris-HCl, pH 8.0, 250mM NaCl, 1mM DTT, 25% glycerol) and stored at 20C in aliquots.

For assembling the stable nsp12-nsp7-nsp8 complex, purified nsp12 was incubated with nsp7 and nsp8 at 4C for three hours, at a molar ratio of 1: 2: 2 in a buffer containing 20mM Tris-HCl, pH 7.5, 250mM NaCl and 4mM MgCl288.

RdRp assay for CoV-2-5-UTR ZnF-site was conducted using a self-priming RNA oligo and one short RNA oligo was used as the primer for such assay for CoV-2-3-UTR ZnF-site containing sequence as template (Supplementary Table3). Oligos were mixed at the following final concentrations in 20L reaction volume: Tris-HCl (pH 8, 25mM), RNA short primer (200M), RNA template (2M), [32P]-UTP (0.1M), BSA (1mg/ml), 0.1M GTP, CTP, ATP and 0.01M UTP and SARS-CoV-2 RdRp complex (~0.1M) on ice. For NEIL2 binding, the indicated concentrations of NEIL2 were incubated in the buffer with RNA on ice for 15min. Reactions were stopped after 15, 30 or 60min by the addition of 20L of a formamide/EDTA (50mM) mixture and incubated at 95C for 10min. Samples were run in a 8% urea PAGE using 1x Tris-borate-EDTA as the running buffer. The gels were exposed to a Phosphor screen for 46h and images analyzed using a Typhoon FLA 7000 phosphorimager (GE Healthcare).

RNA-EMSAs with full length CoV-2 5- and 3-UTRs were carried out as previously described89 with some modifications. Briefly, the 297-nt long 5- and the 200-nt long 3-UTR RNAs (sequences in Supplementary Table2, synthesized and cloned in plasmids by GenScript Inc.) were synthesized by in vitro transcription and end-labeled with [-32P] ATP. The indicated concentrations of components were mixed in 15l reactions containing 0.3% poly (vinyl alcohol) (Sigma, catalog no. P-8136), 2mM MgCl2, 0.1 U RNase inhibitor (Biobharati Life Science, India), 1mM DTT, 20mM HEPES-NaOH pH 7.5, 150mM NaCl, and 20% glycerol, and incubated at room temperature for 5min. The RNA-protein complexes were resolved on a native gel (4% 89:1 polyacrylamide gel containing 2.5% glycerol, 50mM Tris, and 50mM glycine) at 4C for 90min. Our EMSAs were designed to examine both the affinity (when RNA is in trace amount) and the stoichiometry (when RNA is not in trace amount) of the protein component required to form complexes following principles described before90. Hill coefficient was calculated as described before89. RNA-EMSA with short (38-mer) oligonucleotide (oligo) probes were performed as described before36,91, with some modifications. Sequences of the oligonucleotides are listed in Supplementary Table3. Briefly, [-32P]ATP labeled RNA oligos were incubated with 101000nM of purified protein in a binding buffer containing 10mM Tris-HCl buffer (pH 7.6), 15mM KCl, 5mM MgCl2, 0.1mM DTT, 10 U of RNase inhibitor, 1g BSA, and 0.2mg/ml yeast tRNA in a 1020l reaction volume. After a 10-min incubation at room temperature RNA-protein complexes were resolved on a 5% non-denaturing polyacrylamide gel at 120V using 0.5x Tris-borate-EDTA as the running buffer at 4C. For titration assays with short oligos the reaction mix was prepared without yeast tRNA. Gels were fixed in an Acetone: Methanol: H2O (10:50:40) solution for 10min, exposed to a Phosphor screen for 1216h and scanned using Typhoon FLA 7000 phosphorimager.

Lung tissues from freshly euthanized uninfected and SARS-CoV-2 infected hamsters were used for DNA damage analysis. Genomic DNA was extracted using the Genomic tip 20/G kit (Qiagen) per the manufacturers protocol, to ensure minimal DNA oxidation during the isolation steps. The DNA was quantitated by Pico Green (Molecular Probes) in a black-bottomed 96-well plate and gene-specific LA-qPCR assays were performed as described earlier33,39 using LongAmp Taq DNA Polymerase (New England Biolabs, Catalog no. M0323). The LA-qPCR reaction was set for all genes from the same stock of diluted genomic DNA sample, to avoid variations in PCR amplification during sample preparation. Preliminary optimization of the assays was performed to ensure the linearity of PCR amplification with respect to the number of cycles and DNA concentration (1015ng). The final PCR reaction conditions were optimized at 94C for 30s; (94C for 30s, 5560C for 30s depending on the oligo annealing temperature, 65C for 10min) for 25 cycles; 65C for 10min. Since amplification of a small region is independent of DNA damage, a small DNA fragment (~200500bp) from the corresponding gene(s) was also amplified for normalization of amplification of the large fragment. Primer sequences used in the assay are listed in Supplementary Table1. The amplified products were then visualized on gels and quantitated with ImageJ software (NIH). The extent of damage was calculated in terms of relative band intensity with the uninfected control mice/hamster sample considered as 100.

All statistical tests were performed using R version 3.2.3 (2015-12-10). Standard t-tests were performed using python scipy.stats.ttest_ind package (version 0.19.0) with Welchs Two Sample t-test (unpaired, unequal variance (equal_var=False), and unequal sample size) parameters. Multiple hypothesis correction was performed by adjusting p values with statsmodels.stats.multitest.multipletests (fdr_bh: Benjamini/Hochberg principles). The results were independently validated with R statistical software (R version 3.6.1; 2019-07-05). Pathway analysis of gene lists were carried out via the Reactome database and algorithm. Reactome identifies signaling and metabolic molecules and organizes their relations into biological pathways and processes. Kaplan-Meier analysis was performed using lifelines python package version 0.14.6. Violin and Swarm plots were created using python seaborn package version 0.10.1.

Graph generation and analysis of statistical significance between two sets of data were performed with Microsoft excel, GraphPad Software (https://www.graphpad.com/quickcalcs/pvalue1.cfm) and MedCalc statistical software (https://www.medcalc.org/calc/comparison_of_means.php). p=values<0.05 were considered statistically significant.

Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.

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Tracy Morgan and Nas may already be friends, but a new revelation during a DNA test led them to discover an even deeper connection.

During a conversation on the Connect the Dots podcast, Morgan detailed his upcoming appearance on PBS' genealogy showFinding Your Roots, where a DNA test revealed that the rap legend is actually his third cousin.

"I turn the last page, and guess who's sitting there? Nas. Me and Nas is third cousins on my mom's side," Morgan, 55, revealed during the podcast appearance.

"But me and Esco was always tight before that," he added. "I did a show years ago on Comedy Central called One Mic, that was for Nas' mom that just passed away. So me and Esco always been tight."

Morgan who is scheduled to appear on the eighth episode of the PBS series' upcoming tenth season, titled "Mean Streets" revealed that he later called up his longtime pal to share the surprising news.

"When I found out on the West Side Highway, after the show, I called him up and I say, 'Yo Esco,' he said 'What up Trey?' I said, 'Guess what?' He said, 'What?' And I said, 'I just did Finding Your Roots, me and you related,'" Morgan remembered telling Nas, 50.

"He started crying, I started crying. And I said to him, 'If you ever need me, I'm there, cuz.' He said, 'Cuz, if you ever need me I'm there.'"

Stephen Lovekin/FilmMagic

As Morgan noted, the episode is scheduled to air in February, and he learned a few other facts about his familial history in the process.

"They went back 400 years on my father's side and 400 years on my mother's side," he said. "I thought I was big in my life till I found out what my great, great, great grandmother did. My great, great, great, great, great grandfather's name was Abraham Mack. I know the name of the slave masters who owned us I got it right here on my phone and the slave ship."

He continued, "You need to know who you come from before you leave this earth. Know who you are and where you come from. Knowledge itself. I did a lot of crying. And no matter who you are, you're gonna break down."

Never miss a story sign up for PEOPLE's free daily newsletter to stay up-to-date on the best of what PEOPLE has to offer, from juicy celebrity news to compelling human interest stories.

Several other celebrities have appeared on Finding Your Roots, including Maya Rudolph, Neil Patrick Harris and Julia Roberts. And Morgan and Nas aren't the only two famous faces to find a DNA connection through the show.

Back in May, Terry CrewsandBilly Crudup met up for the first time as family after discovering they were relatives. Months before that, Bill Hader detailed an email he received from Carol Burnett where she told him that they were also cousins.

The next season of Finding Your Roots with Henry Louis Gates Jr. will run 10 episodes. It premieres Jan. 2 on PBS.

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Tracy Morgan Discovered He Was Related to Nas After DNA Test - PEOPLE

Scientists from a private laboratory who pioneered forensic DNA testing testified Monday about how and why a condom collected as evidence in 1986 near the body of strangulation victim Ruby Doss was discarded three years later after DNA had been extracted.

The handling of DNA evidence has emerged as a key part of suspected killer Richard Aguirres defense.

The testimony of scientists Michael Baird and Lisa Bennett came during the third week of Aguirres trial in Spokane. The two worked at LifeCodes, a private laboratory on the cutting edge of forensic DNA science in the late 1980s, Baird said.

It was really at the beginning of DNA testing for identification in forensic testing, he said.

Baird was involved in developing the protocols for the lab, he said, which included guards against contamination. The lab was not credentialed in forensic DNA testing in 1989 because, he said, that type of credential did not exist yet.

At the time they used restriction fragment length polymorphism testing, Baird said.

That required a microgram of DNA. Now the most common type of DNA testing requires about 1,000 times less DNA to be successful, Baird said.

Bennett conducted the DNA testing on the condom in 1989. She testified she attempted to obtain a DNA print from the condom.

Upon examining the condom, she could not see any semen with the naked eye, so she put the entire condom into a test tube with clarified water to wash off the DNA from the inside and outside of the condom, which she said was not out of the ordinary for the time.

Bennett then disposed of the condom and continued working with the solution that contained the DNA.

I felt I got all the DNA off that I was going to get off by processing it per protocol, Bennett said.

She attempted to separate the sperm DNA and nonsperm DNA, but ultimately, there was not enough DNA for testing with the technology available at the time, Bennett said.

Aguirres attorney, Karen Lindholdt, questioned Bennett extensively on reagent blanks, which are vials of the solutions used in testing kept separately to show a lack of contamination.

Bennett and Baird said it was not common to keep blanks like that at the time, despite being required now.

While Lindholdt asked numerous questions related to contamination of evidence and documentation of chain of custody, Bennett was firm that nothing was amiss in her handling of the evidence based on protocols of the time.

Im not aware of any case that I violated any protocol processing or that I mixed things, Bennett said.

LifeCodes protocols were not available for the prosecution or defense to examine, which Lindholdt argued made it impossible to know if procedure of the time was followed.

Following the scientists testimony, Lindholdt renewed her motion to exclude the DNA evidence, noting the lack of protocol document and lack of accreditation, among other issues.

The fact is, there were lots of chances for contamination of the condom bag, Lindholdt argued. That lab is the genesis of everything we are talking about in this case, and it was 1989; it wasnt accredited.

Prosecutor Larry Haskell argued that it was the inception of DNA testing, and that time has faded the scientists memories and made the protocol unavailable, all of which could go to the weight of the evidence but not exclude it.

He also noted that more testimony regarding contamination was expected.

Spokane County Superior Court Judge Jeremy Schmidt ruled that evidence will remain admitted and he would consider the issues raised by Lindholdt as part of the weight he will give the evidence.

Huma Nasir, who conducted DNA testing on the case in 2008 while working at private lab Orchid Cellmark, also testified.

She found a full male DNA profile in the condom extracts, Nasir said. That profile later matched Aguirre.

Nasir also found partial male DNA profiles on Doss pants and her left hand. Those did not match Aguirre.

Nasir was unable to obtain a profile from the female portion of the condom extracts.

Additional testimony on the DNA evidence is expected Tuesday. Aguirres trial is scheduled to continue through mid-December.

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On August 3, 1986, at about 5:00 p.m., several citizens were at Crater Lake near FM 3083 and Exxon Rd in the Conroe area when they observed a human body partially submerged in the water. Montgomery County Sheriffs Office Deputies responded, and the body was removed from the water. The body was weighted down with two cement cinder blocks attached to an electrical cord. An autopsy revealed that the unidentified male died from multiple gunshot wounds.

The body was not identified in 1986 and was described as follows: white male 20-30 years of age, about 506, 133 pounds, slight build, collar length reddish brown hair, decaying teeth, tattoos left lower arm (small devil with painted tail), left upper arm (the name Liz), right upper arm (the word Baby Dawn), left ear lobe pierced, wearing mens blue denim jeans size 30 waist, 31 length, brown short sleeve shirt, and mens white athletic type socks.

In 2015, Montgomery County Sheriffs Office Cold Case Detectives exhumed the remains to obtain DNA for entry into the Combined DNA Index System (CODIS), a local, state, and federal database of DNA profiles from convicted offenders, unsolved crime scene evidence, and missing persons, in an attempt to make an identification and facial approximation. Both of these tasks were subsequently accomplished, but neither resulted in an identification.

Due to significant advancements in DNA technology, in May 2023, Cold Case Detectives exhumed the remains a second time to obtain additional DNA for Forensic Investigative Genetic Genealogy (FIGG). The remains were taken to Othram Lab in The Woodlands, where they successfully got more DNA, and began conducting a genealogy assessment. A possible family member was identified in California for targeted DNA testing. Contact was made with the family member by local law enforcement, and a DNA sample was voluntarily obtained and submitted to Othram Lab.

In October 2023, Othram Lab issued a report confirming the familial match between the unknown human remains and the family member in California. The remains have been positively identified as Clarence Lynn Wilson, DOB: 02/18/52, with a last known address in Texas City, Texas.

At this time, the Homicide investigation into Wilsons death is still ongoing. If you know Clarence or have any information, please contact the Montgomery County Sheriffs Office Cold Case Squad at 936-760-5820 or Multi-County Crime Stoppers at 1-800-392-STOP [7867].

Source: Scott Spencer, Lieutenant, Montgomery County Sheriffs Office, Administrative Services

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Advancement in DNA Technology and Tenacity of Cold Case Detective Identifies Victim in 37-year-old Homicide - hellowoodlands.com

Description of datasets

In this study we, utilized five independent cohorts including four cancer- and one Alzheimer-related datasets. Gene expression profiling was done using RNA-seq and DNA methylation data were obtained using the Illumina Infinium HumanMethylation450 BeadChip, measuring DNA methylation levels (beta values) on about 450,000 genomic loci.

The TCGA cohorts were obtained using the TCGAbiolinks package50 (Version2.24.3). TCGA-LUSC22 and TCGA-LUAD23 had clinical and genomic data from 589 and 592 patients, respectively (Supplementary TableS2). Information on the pathological stages of the tumors was included in both datasets. We used this information to stratify the patients into distinct risk groups and compared the resulting stratification with clusters obtained from our approach.

TCGA-LIHC24 was provided by a comprehensive study that included 436 cases with clinical information available in the data. We used the Ishak fibrosis score51 and alpha-fetoprotein (AFP) level52,53,54,55,56 to stratify patients into low-, intermediate-, and high-risk groups. The employed score is described later in this section.

TCGA-L AML was provided by a thorough genomic and epigenomic study on 200 adult cases with AML25. The risk groups were defined based on cytogenetic abnormalities25,57.

In addition, we used the ROSMAP cohort provided by the longitudinal cohort studies of aging and dementia. We downloaded the ROSMAP dataset from accelerating Medicines Partnership- AD58 with Synapse IDs syn3388564 (bulk RNA-seq) and syn5850422 (DNA methylation), using the synapser (https://r-docs.synapse.org/articles/synapser.html) R package (Version0.6.61) and a custom R scripts (Version3.6.1)59.

In the TCGA cohorts, events were defined by patients death and the time to an event referred to the duration from the initial diagnosis to death time or the last follow-up. In the ROSMAP cohort, the event was clinical diagnosis of any dementia including mild cognitive impairment with or without other cognitive conditions, Alzheimers dementia with or without other cognitive conditions, and other primary causes of dementia without clinical evidence of Alzheimers dementia. The time to an event in this context referred to the age at which the first dementiarelated diagnosis was made.

To enhance the power of our network, we included cases that have either a single type of data (i.e., gene expression or DNA methylation) or both data available. In the survival analysis, we included only patients whose gene expression, DNA methylation, and survival data were available (Supplementary TableS2).

The initial step in preprocessing involves normalizing the gene expression data. This is accomplished via a logarithmic transformation in based 10 to stabilize the variance and make the data more amenable to following analyses. Because logarithm of zero is not defined, a small offset is added to the expression levels prior to applying this transformation. iNETgrate further preprocesses data in two steps: cleaning and filtering. The former step involved cleaning DNA methylation and clinical data using the wrapper function cleanAllData(). Loci with more than (50%) missing beta values were removed, while loci with less than (50%) missing values were imputed. The imputation was performed by replacing each missing value with the mean of the beta values for the corresponding locus (preprocessDnam()). The clinical data was subsequently cleaned by removing cases with missing survival time and status (prepareSurvival()). The cleaned survival data had patient information including ID, events, time, and risk based on the clinical gold standard.

The second step in the preprocessing data was filtering out genes and loci that have a weak absolute Pearson correlation with survival time and vital status. This was performed by calling electGenes() inside the cleanAllData() wrapper function. In this study, we set the absolute correlation coefficient cutoffs to 0.2 in all TCGA datasets and 0.1 in the ROSMAP dataset.

Every gene and locus that met the quality control criteria was retained for the subsequent steps. In addition, we used computeUnion() to include corresponding loci of the selected genes and corresponding genes of the selected loci in the next steps of analysis.

In iNETgrate, every node represents a gene with two features (i.e., gene expression and DNA methylation values). Therefore, we needed to calculate the DNA methylation value for each gene using computEigenloci(). This function calculated a weighted average of loci levels for their corresponding gene in the following way. When the number of loci corresponding to a gene was less than six, the first principal component (i.e., eigenloci) was calculated directly by taking a weighted average of beta values using PCA. This was the case for almost (95%) of loci in our datasets (Supplementary Fig.S1).

For the remaining (5%) of cases, in which the number of loci representing a gene was six or more, we used findCore() to determine the most connected cluster of loci for each gene. Specifically, a graph was constructed for each gene using the igraph package (Version 1.5.0). In this graph, each locus is represented by a node. We used a fast greedy algorithm60 to calculate the pairwise correlation between loci and detected communities (i.e., clusters) in the graph. Within each community, the average pairwise correlation was computed. The community with the highest average pairwise correlation was identified as a dense subset of highly co-methylated loci in the graph, and the eigenloci value was then computed based on this subset.

We constructed a network in which nodes represent genes and edges are weighted based on the absolute correlation of gene expression and DNA methylation levels for each pair of genes. This was achieved using the makeNetwork() function. The weight of the edges between genes (g_i) and (g_j) was calculated using the following equation:

$$begin{aligned} mathscr {W}(g_i,g_j)=(1-mu )|{{,textrm{cor},}}_E(g_i,g_j)|+ mu |{{,textrm{cor},}}_M(g_i,g_j)|, end{aligned}$$

(1)

Here, (mathscr {W}(g_i,g_j)) describes the integrated similarity between genes (g_i) and (g_j). The term (|{{,textrm{cor},}}_E(g_i,g_j)|) represents the absolute value of the Pearson correlation between the gene expression levels of genes (g_i) and (g_j). Similarly, (|{{,textrm{cor},}}_M(g_i,g_j)|) represents the absolute value of the Pearson correlation between the DNA methylation levels of these two genes. The hyperparameter (mu ) is an integrative factor controlling the relative contributions of gene expression and DNA methylation data in the network. When (mu =0), the network is based solely on gene expression data. Increasing the value of (mu ) emphasizes the DNA methylation data in the model, whereas (mu =1) indicates that only DNA methylation data is used in calculating the edge weights (i.e., gene similarities).

Construction of the network and identification of the modules were done by the wrapper function makeNetwork(), which first uses the pickSoftTreshold() function (RsquaredCut=0.75) from the weighted gene co-expression network analysis20(WGCNA) package (Version 1.72.1) to determine the optimal soft-thresholding power for our integrated network. Then, the blockwiseModules() function (with minModuleSize=5, the absolute value of Pearson correlation, and the default values for the rest of parameters) is utilized to execute a hierarchical clustering approach. This leads to identification of modules, where each module is a group of genes that exhibit similar patterns of expression and DNA methylation. Additionally, module zero is designed to contain outlier genes that cannot be confidently assigned to any module due to their weak or negligible correlation with other genes.

We employed PCA to compute an eigengene for every module (computEgengenes()). In order to balance the contribution of high-risk and low-risk groups, the gene expression and DNA methylation data were oversampled. Intermediate-risk cases were not included in the PCA. An eigengene is computed from a weighted average of gene expression levels ((E^e)), DNA methylation levels ((E^m)), or both ((E^{em})), using the following equations:

$$begin{aligned} E^e = alpha ^e_{_1} g^e_{_1} + alpha ^e_{_2} g^e_{_2} + cdots + alpha ^e_{_n} g^e_{_n}, end{aligned}$$

(2)

$$begin{aligned} E^m = alpha ^m_{_1} g^m_{_1} + alpha ^m_{_2} g^m_{_2} + cdots + alpha ^m_{_n} g^m_{_n}, end{aligned}$$

(3)

$$begin{aligned} E^{em} = (1-mu ) E^e + mu E^m. end{aligned}$$

(4)

Here, n is the number of genes in the module, (g^e_{_i}) is the expression level of gene i, and (g^m_{_i}) is the methylation level corresponding to gene i (i.e., eigenloci), while (alpha ^e_{_n}) and (alpha ^m_{_n}) are the corresponding weights. These weights are computed using PCA ensuring maximum variance and minimum loss of biological information. The eigengene levels are then inferred for the intermediate-risk group using the same weights obtained from PCA. It should be emphasized that regardless of which eigengenes are used, our network and the corresponding modules are consistently constructed based on both gene expression and DNA methylation data and they depend on the (mu ) hyperparameter. The resulting eigengenes are robust features, carrying useful biological information, which can be leveraged in classification, clustering, and other downstream analyses including survival analysis.

To identify the optimal subset of modules for precise prognostication of risk groups, we conducted a two-step survival analysis using analyzeSurvival(). In the first step, we performed a penalized Cox regression analysis using the least absolute shrinkage and selection operator (lasso) penalty29,30 from the glmnet R package61 (Version 4.1.7). This analysis identified the three modules that were most associated with the survival data. Second, we fitted an AFT model31 to each combination of the top three modules and determined the optimal combination that leads to the smallest p-value. p-values were based on a log-rank test with a null hypothesis that there is no difference between the two high- and low-risk groups62.

To categorize the risk groups, iNETgrate uses findAliveCutoff() that searches for a cutoff on the AFT predictions such that the difference between high- vs. low-risk groups is optimized. More specifically, for each risk group, the function iterates over all possible cutoff values leading to a recall of more than a given threshold (i.e., for low-risk: minRecall=0.2, for high-risk: minRecall=0.1 in ROSMAP and 0.05 in other datasets) and selects the cutoff value that maximizes precision.

To ensure the reliability of our integrative approach, we performed a comparative analysis by benchmarking our results against alternative methodologies including a well-known patient similarity network called SNFtool. We also compared our results vs. risk classification according to the clinical gold standards based on the intrinsic nature of the disease in each cohort.

The SNFtool first computes a similarity matrix for each data type (i.e., gene expression and DNA methylation). That is, using each data type independently, a network is constructed where nodes are patients and weights of the edges represent similarity between patients computed based on correlation. The networks (similarity matrices) are then fused to create a consensus network representing the overall similarity between patients across different data types. The resulting patient similarity network is then used to cluster patients into subgroups. We noted that the SNFtool faced some limitations in using all the DNA methylation loci due to memory exhaustion while computing the similarity matrices. We tackled this issue by filtering out loci with a relatively low variation characterized by a standard deviation of less than 0.1. Determining the appropriate cutoff for a given dataset is subjective and challenging for SNFtool users.

In lung cohorts (LUSC and LUAD), we evaluated the risk groups based on the tumor stage. Specifically, we classified stages I,IA,IB,II, and IIA as the low-risk group, stages IIIB and IV as the high-risk group, and the remaining stages as the intermediate-risk group. In the LIHC cohort, we considered a case high-risk if the AFP level was greater than 500 or the Ishak fibrosis score was six. In contrast, patients were considered low-risk if their AFP levels were smaller than 250 and their Ishak fibrosis scores were 0, 1, or 2. The remaining cases were considered intermediate-risk. In the LAML cohort, we utilized the classification system available in the clinical data that categorized cases based on cytogenetic criteria into three groups of favorable (low-risk), intermediate, and poor (high-risk). We utilized the Braak score63 to stratify the ROSMAP cohort into three risk groups. Cases with a Braak score of 0, 1, or 2 were considered low-risk, those with a Braak score of 5 or 6 were classified high-risk, while the remaining cases were grouped as intermediate-risk.

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Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package ... - Nature.com

A former Washington State Patrol Crime Lab employees DNA was found on Ruby Doss shirt and jacket after he examined the items without gloves, a forensic scientist testified Tuesday.

Its the third week of former Pasco police officer Richard Aguirres bench trial over the killing. Doss was found beaten and strangled in an industrial area off of East Sprague Avenue.

Anna Wilson, a forensic scientist at the crime lab, testified Tuesday afternoon about testing Doss jacket and blouse for DNA in October 2019.

Wilson swabbed the collar area of the jacket and blouse, along with the pussycat bow on the blouse. Then she used a device similar to a carpet cleaner to squirt water on the area and then suck it back up, she said.

That hydrates any DNA cells, which are then collected in a solution and tested.

I was specifically told to target the area that may have been touched by a perpetrator if they strangled the victim, Wilson said.

She found a mixture of DNA from three people on both items.

On the jacket, Doss DNA was 48%; William Morig, a forensic technician who examined the items in 1986, contributed 49%; and an unknown contributor had 3% of the sample.

The blouse sample was 35% Doss, 61% Morig and 4% unknown contributor.

Wilson said she saw a photo of Morig examining the clothing without gloves.

Our current standard, they were not following, Wilson said.

DNA testing was extremely new in 1986, and scientists did not understand fully how DNA transferred.

It was standard practice not to take the precautions not to leave DNA, Wilson said.

All former crime lab employees have their DNA entered into a database to be cross-referenced because of this issue, Wilson said.

The unknown samples, Wilson said, did not contain enough DNA to be entered into the national DNA database.

Aguirre was excluded as the third contributor.

A state lab employee, Jeremy Sanderson, testified about being asked to check other labs work and evaluate if the DNA profiles were enough to upload into the Combined DNA Index System (CODIS) database in 2009.

If not enough DNA markers are found in the sample, it cant be entered into CODIS, Sanderson said.

Three partial male profiles were found in Doss underwear, the waistband of her pants and testing from other parts of her body. The profiles were found in a mixture of DNA in which Doss was the predominant source. The partial profiles were too limited to enter into CODIS, he said.

They were eligible for comparison to specific DNA profiles, he said.

Michelle Galusha testified about her analysis of the condom extracts in 2002 while working at Bohdi, another private lab. She was able to develop the first full profile from the sperm portion of the DNA taken from the condom.

She was unable to develop a profile from the non-sperm portion.

Other labs went on to do similar work, as witnesses testified to Monday.

Aguirres trial is set to continue through mid-December.

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Forensic scientist testifies about evidence contamination at state lab in 1986, says knowledge of DNA has since ... - The Spokesman Review

Tracy Morgan just found out that a branch on his family tree belongs to Nas.

During the kick-off episode of the "Connect The Dots" podcast, the "SNL" alum revealed that he and the "Made You Look" hitmaker share more than just friendship; they are cousins, as reported by Today.

The comedian says the startling revelation came while appearing on a segment of the PBS docuseries "Finding Your Roots" set to air on Feb. 20, 2024 that the two are actually kin.

I turn the last page, and guess whos sitting there? Morgan said while reflecting on that moment. Nas. Me and Nas are third cousins on my moms side.

Morgan also said his bond with the rapper transcends the limelight.

Me and Esco was always tight before that, he explained. I did a show years ago on "Comedy Central" called One Mic, that was for Nas mom that just passed away. So me and Esco always been tight.

After Morgan got the news of his newfound family member, he reached out to Nas to notify him.

I called him up, and I say, Yo Esco, he said, What up Trey? Morgan recalled. And I said, I just did 'Finding Your Roots.' Me and you related.

While sharing the news, both became overtaken with emotion.

He started crying, I started crying, Morgan recalled. And I said to him, If you ever need me, Im there, Cuz. He said, Cuz, if you ever need me, Im there.

Additionally, during the show, Morgan will trace his ancestors' footsteps after they arrived in the United States from slave ships.

They went back 400 years on my fathers side and 400 years on my mothers side. I thought I was big in my life till I found out what my great, great, great grandmother did, Morgan continued. My great, great, great, great, great grandfathers name was Abraham Mack. I know the name of the slave masters who owned us, I got it right here on my phone and the slave ship.

From the The Last O.G. actors powerful experience with discovering truths within his lineage, he hopes more people will take charge and do the same.

You need to know who you come from before you leave this earth. Know who you are and where you come from. Knowledge itself. I did a lot of crying. And no matter who you are, youre gonna break down.

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Tracy Morgan Discovers He Is Related To Nas Through DNA Test - BET

The most comprehensive analysis of Indigenous Australians genomes collected to date has revealed an abundance of DNA variations some of which have never been reported anywhere else in the world paving the way for new, tailored treatments that address health inequities for Aboriginal and Torres Strait Islander peoples. A team of Australian researchers, involving scientists from The Australian National University (ANU),The University of Melbourne and the Garvan Institute of Medical Research, found DNA differences between Indigenous Australians in the Tiwi Islands and those in the Central Desert are greater than anywhere else in the world outside of Africa.

The researchers detected millions of small genetic differences and hundreds of thousands of much larger structural variants that affect segments of DNA. These variants occur naturally in different individuals of a population and make up most of the genetic differences between individuals. They may also be linked to diseases in some families. These DNA sequences show a level of genetic variation not observed anywhere else in the world outside of Africa, reflecting Aboriginal and Torres Strait Islander peoples deep cultural and linguistic diversity and long-standing connection to the Australian continent, Dr Hardip Patel, from ANU, said. Some of the DNA variations we discovered appear to be exclusively found in Indigenous Australians, while others appear to be found in just one out of the four Indigenous communities that we consulted and worked with. Previously weve had to try to utilise the DNA of non-Indigenous populations to help diagnose and treat disease among Indigenous Australians, which has proven difficult and is often less reliable. But now we have a new, more representative genomic dataset to build off. Under the leadership of the National Centre for Indigenous Genomics (NCIG) at ANU, research teams examined the DNA of up to 159 Indigenous Australians from four Aboriginal communities in the Central Desert, Far North Queensland and three islands off the coast of the Northern Territory the Tiwi Islands and Elcho Island. Its hoped the research will improve health outcomes for Indigenous Australians by enabling tailored treatments for a range of conditions including diabetes, coronary disease and cancer all of which disproportionately impact Indigenous peoples compared to the rest of the Australian population. Aboriginal people have long said you cant treat us the same because we are so different. Having scientific proof to show this is true is remarkable, ANU Associate Professor Azure Hermes, a proud Gimuy Walubara Yidinji woman and deputy director of NCIG, said.

Clinicians must realise treatment options for Indigenous Australians cant be viewed through a one-model-fits-all lens. We are not a single genetic group and cant be lumped into one category.

Professor Stephen Leslie, from The University of Melbourne, said: Genomics enables us to look back through time at aspects of human history. This history has a direct bearing on the genetic variation we see today.

As scientists we were keen to ensure that Indigenous Australians took the lead on shaping how these questions were approached and how their data was used. Working with NCIG provided the framework to enable this, for which we are very grateful.

Dr Ashley Farlow, also from The University of Melbourne, said: These genomic patterns allowed us to make predictions about the most effective ways to build genomic resources for Australian Indigenous populations in the future.

We identified more than 160,000 structural gene variants, which is more than any previous population-level, long-read study to date,"Dr Ira Deveson, from the Garvan Institute of Medical Research, said.

The research team discovered at least 300 structural variants in each individual that appear to be unique to Indigenous Australians. A genome is equivalent to an instruction manual for the body. It is a blueprint that contains all the genetic information we need to grow, develop, function and respond to the environments in which we live. Genomics medicine harnesses a populations genetic information to help individuals and communities prevent, diagnose and treat a range of complex conditions, as well as rare genetic disease. The code embedded in our genome is unique to each individual its what makes us different to other human beings. Variations within our genetic code can not only contribute to the way we look but can sometimes impact our risk of developing certain diseases, Dr Patel said. We still dont understand why Aboriginal people are more prone to certain health conditions such as kidney disease, diabetes, coronary disease, cancer and other conditions. But genomics might be an important piece of the puzzle that helps unlock some of these answers. Associate Professor Hermes said the project is also about giving Indigenous communities oversight of how their genetic information is used by science. Our goal is to work with and empower Indigenous Australians to take ownership of their genetic information and show them the power of genomics and the health benefits it can deliver, Associate Professor Hermes said. Its taken us almost eight years to get to this point and has only been made possible because of guidance by Indigenous communities, careful consultation, building relationships with communities and understanding their priorities and protocols.

The research is published in two separate papers in Nature.This work was a collaboration between ANU and a number ofinstitutions from across the country.

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DNA discovery opens door to tailored medicine for Indigenous Australians | Australian National University - ANU