TEAL-seq: Targeted Expression Analysis Sequencing

TEAL-seq: Targeted Expression Analysis Sequencing | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results TEAL-seq: Targeted Expression Analysis Sequencing Georgia Doing , Priya Shanbhag , Isaac Bell , Sara Cassidy , Efthymios Motakis , Elizabeth Aiken , Julia Oh , View ORCID Profile Mark D. Adams doi: https://doi.org/10.1101/2024.11.26.625462 Georgia Doing 1 The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr , Farmington, CT 06032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Priya Shanbhag 1 The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr , Farmington, CT 06032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Isaac Bell 1 The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr , Farmington, CT 06032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sara Cassidy 1 The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr , Farmington, CT 06032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Efthymios Motakis 1 The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr , Farmington, CT 06032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elizabeth Aiken 1 The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr , Farmington, CT 06032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Julia Oh 1 The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr , Farmington, CT 06032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mark D. Adams 1 The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr , Farmington, CT 06032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mark D. Adams For correspondence: Mark.Adams{at}jax.org Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Metagenome sequencing enables genetic characterization of complex microbial communities. However, determining the activity of isolates within a community presents several challenges including the wide range of organismal and gene expression abundances, the presence of host RNA, and low microbial biomass at many sites. To address these limitations, we developed “targeted expression analysis sequencing” or TEAL-seq, enabling sensitive species-specific analyses of gene expression using highly multiplexed custom probe pools. For proof-of-concept we targeted about 1,700 core and accessory genes of Staphylococcus aureus and S. epidermidis , two key species of the skin microbiome. Two targeting methods were applied to laboratory cultures and human nasal swab specimens. Both methods showed a high degree of specificity, with >90% reads on target, even in the presence of complex microbial or human background DNA/RNA. Targeting using molecular inversion probes demonstrated excellent correlation in inferred expression levels with bulk RNA-seq. Further, we show that a linear pre-amplification step to increase the amount of nucleic acids for analysis yielded consistent and predictable results when applied to complex samples and enabled profiling of expression from as little as 1 ng of total RNA. TEAL-seq is much less expensive than bulk metatranscriptomic profiling, enables detection across a greater dynamic range, and uses a strategy that is readily configurable for determining the transcriptional status of organisms in any microbial community. IMPORTANCE The gene expression patterns of bacteria in microbial communities reflect their activity and interactions with other community members. Measuring gene expression in complex microbiome contexts is challenging, however, due to the large dynamic range of microbial abundances and transcript levels. Here we describe an approach to assessing gene expression for specific species of interest using highly multiplexed pools of targeting probes. We show that an isothermal amplification step can allow profiling of low biomass samples. TEAL-seq should be widely adaptable to the study of microbial activity in natural environments. INTRODUCTION As microbiome research matures, studies are shifting from those that establish correlations between microbial profiles and disease states to those that define the mechanisms by which microbes impact host physiology ( 1 , 2 ). Metagenome shotgun sequencing (mWGS) analysis has been a powerful discovery tool, but it cannot address the functional state of constituent microbes. Measurement of microbial gene expression in a natural context is key to understanding the mechanistic forces driving commensalism and pathogenesis within community and host relationships ( 3 – 6 ). Changes in diet, drug treatment, introduction of a pathogen, or alteration in host pathways may change the activity of the microbiome, not just its composition ( 6 , 7 ). Metatranscriptome (metaTx) profiling can uncover which organisms are transcriptionally active, and which genes are expressed. However, the efficacy of metaTx is limited by the large dynamic range of organismal abundance, gene expression levels, and—in some cases—the presence of host transcripts. Thus, quantitation of gene expression in lower abundance organisms by untargeted metaTx sequencing remains infeasible due to the cost of sequencing to sufficient coverage depth. A few metaTx studies have been published that illustrate this point. One study evaluated the contribution of vitamin B12 production by skin microbes to the development of acne ( 8 ). Another examined transcription of the fecal microbiome of adult men ( 9 ). Both studies required ∼100 million reads/sample. New, more accessible methods are needed to transcriptionally profile microbes of interest in their natural milieu. The skin is an ideal proving ground for the development of new technologies that measure microbial gene expression in mixed communities. The abundance of bacterial species can vary widely at different skin sites but importantly, abundance is a poor prognosticator of functional importance ( 10 – 12 ). At most skin sites host cells contribute over 90% of the total extracted nucleic acids, making mWGS and metaTx extremely inefficient, particularly for characterizing lower abundance organisms ( 13 ). Staphylococcus (S.) spp. are keystone contributors to cutaneous immunity, barrier integrity, and microbial community homeostasis, including antagonism with skin pathogen S. aureus ( 14 – 17 ). Our previous metaTx work with skin swabs, however, yielded <5% of reads corresponding to microbial transcripts, highlighting the challenges of studying these communities using common methods (unpublished). Targeting microbial profiling to specific genes or genomes enables more cost-effective and comprehensive evaluation of microbial composition and activity. Array-based ( 18 – 20 ) and capture-based ( 21 – 24 ) strategies have been used to assess bacterial and viral abundance, but those methods are either difficult to customize, expensive, laborious, or all three. The availability of low-cost oligonucleotide pools from multiple vendors offers assay development options based on highly multiplexed custom-designed probes, including the ability to iteratively optimize pools based on performance and facile incorporation of new content. We previously developed MA-GenTA (Microbial Abundances from Genome Tagged Analysis) as a quantitative and cost-effective method for species-level microbial profiling ( 25 ). We used highly multiplexed (>16,000) oligonucleotide probes to derive relative abundance data for microbes from the mouse gut. MA-GenTA and mWGS data demonstrated excellent correlation down to 0.01% relative abundance ( 25 ) and enabled inference of gene pathway composition at a cost only modestly higher than 16S rRNA amplicon sequencing. Here we build on this experience by extending the methods to targeted gene expression profiling. RESULTS Experimental design We selected Staphylococcus aureus ( Sa ) and S. epidermidis ( Se ) for developing targeted transcriptome profiling methods as they are keystone species in the skin microbiome, important for human health and disease, and well-characterized genomically and functionally ( 26 – 28 ). Two targeting strategies were pursued: a single-primer extension (SPE) method based on MA-GenTA ( 25 ) and a molecular inversion probe (MIP) method, previously published for high-plex SNP genotyping assays ( 29 ). SPE uses a single 40-base primer that anneals to target sequences to prime DNA synthesis on a partially constructed Illumina sequencing library. MIP involves hybridization of the probe to the target, followed by extension across the gap region, ligation, and amplification of closed circles using primers with Illumina-compatible tails. Both methods target a cDNA or genomic DNA template. We established experimental conditions to evaluate each targeted sequencing approach. Initial tests used RNA and genomic DNA isolated from pure cultures of Se, Sa , mixtures of the two, and controls that should not contain target sequences. We compared technical reproducibility, sensitivity to RNA input amount, specificity of targeting in mixed samples, the impact of rRNA depletion, the effect of pre-amplification of cDNA prior to capture, and comparison of targeted RNA-seq methods with standard bulk RNA-seq ( Figure 1 ). Download figure Open in new tab Figure 1. Layout of the experimental design. The experimental variables used for evaluating the targeted RNA-seq approaches are shown, illustrating how RNA samples were processed for analysis to determine the optimal TEAL-seq method. Probe pools are >94% on-target and species-specific Probe design for each species maximized within-species binding and limited cross-species binding (see Methods). The SPE design encompassed 6,121 probes targeting 1,723 Sa coding sequences (CDS) and 5,879 probes targeting 1,670 Se CDS ( Figure 2A ). The MIP design encompassed 4,960 probes targeting 1,694 Sa CDS and 4,863 probes targeting 1,748 Se CDS ( Figure 2B ). A combined pool of Se and Sa probes was synthesized for each targeting method. Download figure Open in new tab Figure 2. Distribution of number of probes per gene in the targeted strains. A) Probes target >50% Sa and Se CDS in SPE with at least one unique probe. B) Probes target >50% Sa and Se CDS in MIP with at least one unique probe. C) A high proportion of reads map on target with when tested on DNA and RNA controls. Given the low biomass of the microbiome at many body sites, we evaluated the use of single-primer isothermal amplification (SPIA) ( 30 ) to increase the amount of template available for targeting and library construction. A theoretical advantage of targeted sequencing is that it makes depletion of rRNA unnecessary; we therefore also tested the effect of rRNA depletion on both bulk and targeted RNA-seq. For both SPE and MIP, 94-99% of reads map to the targeted regions across all experimental conditions, indicating a high efficiency of targeting ( Figure 2C , Supplemental Table 1). Notably, no ribosomal (r)RNA reads were detected, even in samples processed without rRNA depletion. Single-species experiments showed that probes were highly specific to the targeted genome when full length reads were aligned to the reference genome. In addition, few reads were obtained from E. coli RNA, human genomic DNA, and mouse microbiome RNA (cDNA) (data not shown), indicating that cross-hybridization is minimal, even in the presence of complex microbial nucleic acids. Targeted transcriptomics is reproducible We tested the reproducibility of inferred gene expression levels based on counts per million (CPM) per probe using MIP and SPE by comparing variation across technical replicates to that of bulk RNA-seq. We found technical replicates (libraries prepared from the same RNA sample) highly correlated for all sequencing approaches ( Figure 3A ). Subsequently, we determined that libraries from the same RNA sample with and without SPIA treatment were highly correlated for bulk RNA-seq and MIP, but less so for SPE ( Figure 3B ). Further, when SPIA was used, libraries prepared from standard amounts of RNA (“Standard Input”, 10 ng) and an order of magnitude less RNA (“Low Input”, 1 ng) were still correlated for both MIP and SPE ( Figure 3C ). Download figure Open in new tab Figure 3. Evaluation of reproducibility using a 50:50 mixture of Sa and Se RNA. A) Technical replicates (n=2) are highly correlated for bulk RNA-seq, MIP and SPE. B) Samples with and without SPIA have good correlation for bulk RNA-seq and MIP, but less so for SPE. C) Samples with low (1 ng) and standard (10 ng) input total RNA amounts correlate well in MIP and SPE. Correlation coefficients ( R ) and p-values from Pearson tests. Probe performance varies across CDS Targeted sequencing of Se and Sa gDNA was used to evaluate the extent of variation in probe performance. We observed a wide range of CPM/probe, with Se probes having a much broader range than Sa probes in both the SPE and MIP datasets ( Figure 4A ). Since Se and Sa probes were synthesized as a single pool, there is no a priori reason why they should have such different performance between the two species. We considered whether there is a gradient of genomic copy number ranging from the origin to the terminus of replication due to differences in growth rate ( 31 ), however, we found that genome position was poorly correlated with CPM levels in both species ( Supplemental Figure 1 ). Download figure Open in new tab Supplementary Figure 1. CPM bias based on gene position relative to origin of replication. There is no substantial bias introduced by position relative to the origin of replication for Se Tu3298 (A) or Sa USA300 (B). Download figure Open in new tab Figure 4. Probe performance varies and correlates with melting temperature in SPE data more so than MIP data. A) Sequencing of gDNA with SPE shows a range of CPMs for Sa (black) and Se (grey) probes. B) Probe CPM correlates with melting temperature in SPE data for both Sa (black) and Se (grey). C) CPM has minimal correlation with melting temperate in MIP data for both Sa (black) and Se (grey). For SPE probes, there was a marked increase in average CPM with probe melting temperature (T M ) ( Figure 4B ), whereas T M had minimal correlation with CPM for MIP probes ( Figure 4C ). We also considered the performance of probes that have mismatches to the test genome compared to perfect-match probes. This is an important consideration for profiling of microbial communities, because the exact sequence of the target genomes will not often be known. SPE probes showed a substantial reduction in read counts with increasing numbers of mismatches, whereas MIP showed little or no effect of up to three mismatches ( Supplemental Figure 2 ). Given the limitations of the SPE approach, we focused our remaining analyses on the MIP datasets. Download figure Open in new tab Supplemental Figure 2. Read mapping to experimental genomes. Mapping rates as the number of mismatches between the probe design sequence and the experimental genome increase for SPE (A) and MIP (B). Targeting strategies are compatible with signal amplification The inability to perform oligo-dT priming for cDNA synthesis from microbial transcripts means that rRNA must be depleted as part of library construction for bulk RNA-seq. However, targeting offers the possibility of using total RNA as input, thus reducing processing steps and any potential bias introduced by rRNA depletion. We tested the fidelity of targeting (MIP) with and without ribodepletion and compared the results to bulk RNA-seq. There is only slightly lower correlation in CPM between samples sequenced by bulk RNA-seq and MIP (mean R 2 =0.47, Figure 5A for Sa , Figure 5B Se ) than there are between MIP samples with and without ribo-depletion (mean R 2 = 0.64, Figure 5C for Sa , Figure 5D Se ), indicating that rRNA depletion had a substantial impact on many non-ribosomal transcripts. Download figure Open in new tab Figure 5. Sources of variation in targeted RNA-seq. A) comparison of CDS-level expression in bulk RNA-seq and MIP (targeted) without SPIA (black) and with SPIA (grey) for Sa and Se (B). C) MIP with and without RNA depletion without SPIA (black) and with SPIA (grey) for Sa and Se (D). E) MIP with and without SPIA amplification with ribo-depletion (black) and with ribo-depletion(grey) for Sa and Se (F). G) Quantification of sum of squared error (SSE) across comparisons for Sa and Se (H). Correlation coefficients ( R ) and p values from Pearson tests. **** p<0.0001, Wilcoxon test. To assess SPIA’s influence on resulting mRNA measurements, we quantified mRNA abundance as inferred from bulk RNA-seq versus MIP with and without SPIA. Bulk RNA-seq and MIP samples with and without SPIA correlated highly (mean R 2 = 0.83, Figure 5E for Sa , Figure 5F Se ), more highly than samples with and without rRNA depletion. Further, CDS-level CPM values across bulk and targeted RNA-seq were less variable when SPIA amplification was used ( Figure 5A , B grey) than when SPIA was not used ( Figure 5A , B black). In summary, since SPIA amplification introduces significantly less sum squared error (SSE) than ribo-depletion does (p<0.0001 by Wilcoxen test) for both species ( Figure 5G and 5H ), we determined MIP with SPIA to be the optimal workflow for TEAL-seq (as outlined in Figure 1 ). TEAL-seq captures gene expression changes across growth conditions A major goal of RNA-seq analysis is to identify differentially expressed genes (DEGs) across conditions of interest to infer underlying biology and nominate targets for downstream functional studies. To test the ability of TEAL-seq to capture DEGs under physiologically relevant conditions, we measured gene expression from Sa and Se cultures grown in TSB (pH 7), acidic TSB (pH 4.8), and TSB with urea (4.5%w/v) to model acid stress commonly encountered by staphylococci in vivo . Growth conditions were delineated by the first and second PCs which together explained 97% of variance for Sa ( Figure 6A ) and 94% of variance for Se ( Figure 6B ). Further, genes with the highest fold-change in response to acid were consistent with known acid responses, in particular urease genes in Sa ( Figure 6C ) and genes previously identified as upregulated by acid in Se ( Figure 6D ). Clustering of Sa samples shows that the expression patterns are driven first by condition ( Figure 6E ). For Se , condition also influences expression of these genes of interest, although urea treatment had less of an effect than acid ( Figure 6F ). Our results suggest that TEAL-seq performs predictably for the analysis of differential gene expression and thus is a valuable approach for comparative microbial studies. Download figure Open in new tab Figure 6. TEAL-seq detects urease and acid response genes as differentially expressed under acid stress. A) Principal components of targeted gene expression separate samples by growth condition for Sa and Se (B). C) Differential expression between control (TSB) and acid treatment identifies urease genes for Sa ( red) and the previously published acid response for Se (red and blue) (D). E) Urease and acid response probes across conditions are consistent across CDSs for Sa and Se (F). Each column represents biological replicates (n=3). DEG analysis from model and human samples To model the technical challenge of low amounts of material extractable from skin microbiomes, we applied TEAL-seq to RNA extracted from reconstructed human epidermis (RHE) colonized with clinical skin microbiome isolates as well as RNA extracted directly from human nasal swabs. Sa and Se transcripts were readily detected from both samples. Transcript levels derived from these sources were substantially different from those grown in TSB (PC1, separation by sample type accounted for 37% of variance for Sa ( Figure 7A ) and 46% of variance for Se ( Figure 7B ). Notably, samples from independent RHE and swabs were more variable than replicates of growth in TSB, as expected for complex models and ex vivo samples. Further, there was high correlation in expression profiles for samples grown in TSB or between sequencing replicates of the same sample for both Sa ( Figure 7C ) and Se ( Figure 7D ). Thus, this variation is likely biological. Download figure Open in new tab Figure 7. Sa upregulates urease gene expression when grown on RHE and as sampled in nasal swabs, but Se does not. A) PCAs separate expression from growth on RHE from in vitro (TSB) and human nasal swabs in Sa B) and Se. C) Samples correlate by source more than by SPIA treatment in Sa D) and Se . Values are pairwise distances in Pearson correlation matrices. E) Urease genes (operon structure and direction per diagram) are induced on RHE and in nasal swabs for Sa F) but not Se . We used TEAL-seq to compare expression patterns of urease genes from bacteria grown in TSB, on RHE, or isolated from swab samples. A subset of both nasal swabs and RHE samples showed marked increase in the expression of urease genes relative to TSB for Sa ( Figure 7E ). Interestingly, another subset of swab and RHE samples had lower expression of urease genes compared to TSB for Sa . Se samples from RHE and swabs also displayed variable urease gene expression compared to those grown in TSB, although the trend was less pronounced than in Sa samples ( Figure 7F ). To further validate TEAL-seq as a faithful measure of mRNA in complex samples, we attempted to compare the expression of urease genes by TEAL-seq to that detectable by bulk RNA sequencing on the same RHE sample. Notably, not all samples had detectable microbial mRNA using bulk RNA sequencing; colonized RHE samples sequenced to depths of 8.5 – 42.1 million (m=30.4 million) contained only 0.06 – 1.5% (m=0.3%) reads that mapped to Sa or Se mRNA and thus failed to provide sufficient reads to measure genome-wide expression (Supplemental Table 3). These results demonstrate that TEAL-seq can be used to measure gene expression of in vivo relevant pathways in complex samples in a manner more sensitive than the current state-of-the-art method. Discussion We present an approach for targeted gene expression analysis optimized for samples with low biomass—TEAL-seq—that yields a high proportion of reads on target, even in the presence of total RNA input and complex background RNA or DNA. We tested two oligo-based methods for targeting, MIP and SPE, and determined MIP to be more robust than SPE. Both methods were capable of inference of mRNA levels relative to bulk RNA-seq, but expression levels inferred from MIP had less probe-to-probe variation. Further, we showed that cDNA amplification using SPIA reduced variation between replicates and had less impact on inferred expression level than rRNA depletion. Therefore, we propose the use of MIP with SPIA for measuring bacterial transcript levels in low biomass samples. We showed that probes designed based on genes of laboratory strains captured gene expression information from other common experimental strains and clinical isolates of the same species. This is in part due to short probe length, ensuring coverage of conserved genes across strains, as well as the design, which enabled integration of data across multiple probes per CDS. Cross-strain capture is an important feature of this method as the application of TEAL-seq to measure gene expression of real-world samples will require probes to be designed from existing genomic or metagenomic data. Our benchmarking of results from probes designed for Sa strain M2872 and Se strain ATCC14990 and applied to Sa strain USA300 and Se strain Tu3298 as well as clinical isolates of both species demonstrate the flexibility of TEAL-seq. We used TEAL-seq to assess changes in gene expression of Sa and Se grown in TSB at pH 7 and pH 4.8 and identified highly concordant changes in expression compared to previous studies. We confirmed that Sa responds to acid stress with urease genes and Se does not. This is significant as urea is a human skin-produced metabolite that has important roles in barrier defense ( 32 ), and differences in Sa and Se responsiveness to acidic conditions may explain differences in invasiveness ( 33 ). MetaTx has had limited usage in studying low biomass microbiomes. However, TEAL-seq successfully captured predictable expression profiles from multiple complex sample types, including those isolated clinically. New tools are needed for identification of population-level expression of virulence pathways or immunogenic metabolites at species resolution, as these will contextualize the function of the microbiome as a collective and refine our understanding of the contribution of individual species to the community ( 34 – 37 ). Additional studies applying TEAL-seq in the context of complex natural microbial communities will be needed to further demonstrate the potential of this method in experimentally relevant systems. However, the robust performance of TEAL-seq in capturing biologically significant transcriptional patterns from skin microbes grown under skin-relevant stress and reconstructed human epidermis make it a promising tool for future applications. Methods Bacterial samples and RNA and DNA isolation Single colonies of Staphylococcus aureus USA300 and Staphylococcus epidermidis Tu3298 were inoculated into 2ml Tryptic Soy broth (TSB) (BD cat:211825) and grown overnight at 37°C and 200 rpm agitation. Overnight cultures were sub-cultured into fresh 2 ml TSB medium at OD 600 ∼0.05 and allowed to grow to OD 600 ∼0.5-0.6. For stress conditions, as in ( 38 ), overnight cultures were grown as described above and then sub-cultured into mildly acidic TSB (pH 4.8) or TSB+ 4.5% w/v urea and allowed to grow to OD 600 ∼0.5-0.6. For RNA extraction, to 1 ml of each mid-log phase culture, 2 ml of RNAprotect® Bacteria Reagent (cat # 76506, Qiagen Inc.) was added, and cells were centrifuged at 8000 x g for 5 minutes at 4°C. Cell pellets were then resuspended in RLT buffer (RNeasy Mini kit, (cat # 74104, Qiagen Inc.) and transferred to 2 ml safe-lock tubes containing 50 mg acid-washed glass beads (150– 600 μm diameter). Cells were homogenized using a TissueLyser for 4 minutes at 30Hz/sec and further processed according to the kit protocol. For DNA extraction, cells from 1 ml of culture were pelleted by centrifugation at 16000 x g for 2 minutes and DNA extracted using GenElute™ Bacterial Genomic DNA Kit (cat# NA2110, Sigma-Aldrich). For mixed cultures, overnight cultures of individual strains were sub-cultured as described above and allowed to grow to an OD 600 ∼ 0.5-0.6 in TSB at 37°C with 200 rpm agitation. Equal numbers of cells from each strain were then inoculated into fresh 5 ml TSB at an OD 600 ∼0.1-0.2 and grown at 37°C with agitation at 200rpm for 2-3 hours until the OD 600 reached ∼0.5. Mouse microbiome DNA was prepared as previously described ( 39 ). RNA was extracted from bacteria grown in association with a reconstructed human epidermis (RHE) skin organoid model (MatTek EpiDerm) with the Qiagen RNeasy kit (cat # 74104, Qiagen Inc.) and Escherichia coli RNA was purchased from ThermoFisher (#AM7940). Reconstructed human epidermis cell culture cultivation 9mm primary normal human RHE tissue cultures were obtained from MatTek Corporation (EpiDerm, Gothenburg, Sweden). All batches were grown using cells from MatTek Corporation’s EpiDerm standard donor, a healthy male. The RHE were cultured according to the manufacturer’s directions. Briefly, upon arrival, the RHE cultures were placed in 6-well plates with 1mL of warmed antibiotic-free EpiDerm Maintenance Media (MatTek Corporation,) or the equivalent EpiDerm Assay Media basally per well. The basal media was replaced daily, and the RHE cultures were kept at 37°C with 5% CO 2 . For each microbial isolate, a single colony was grown overnight in sterile 1X TSB. 10 8 colony-forming units from each liquid culture were washed with ultrapure water (Fisher Scientific, #AAJ71786AP, Hampton, NH) and resuspended to a final concentration of 10 7 colony-forming units in 120 µL. The RHE cultures were then dosed with 120uL of microbial isolate or vehicle (ultrapure water alone). Dosed RHE cultures were incubated for 1 hour at 37°C, then inoculum was aspirated to restore the air liquid interface. RHE were incubated with remaining bacteria for 18 hours prior to harvest. At harvest, 200 µL of PBS (MatTek Corporation) was added to the apical surface of each RHE culture, pipette mixed, removed, and plated for CFU enumeration. 140 µL of RLT buffer + 1% beta-mercaptoethanol was added to each RHE culture for RNA preservation. The RLT buffer-tissue solution was frozen at-80°C until RNA extraction. S . epidermidis Tü3298-GFP colonized RHE were visualized under blue light. Target gene selection and probe design Pangenome datasets for Sa and Se were assembled using Roary ( 40 ) based on the available complete genome sequences of each species: 51 complete Sa and 86 complete Se genomes downloaded from GenBank. CDS present in at least 50/86 Se and 30/51 Sa genomes were submitted for probe design. Candidate probe sets were designed using standard informatics workflows at Tecan Genomics for SPE probes and Molecular Loop for MIP probes. We selected probes for inclusion in the custom pool by subsampling based on perfect BLAST matches to at least 50 Se or 30 Sa genomes and exclusion of probes with alignments having fewer than five mismatches to the alternative species or to a database of 10 S. capitis and 15 S. hominis genomes. Additional filtering criteria removed probes at the extremes of melting temperature and sequence composition (requiring no runs of >6 of a single base) and established minimum probe spacing of at least 100 bases. All probes targeting both genomes were synthesized and used in a single pool. Distributions of the number of probes per CDS is provided in Figure 2 . Probe sequences are in Supplemental Table 2. Purified nucleic acids were split across several workflows as shown in Figure 1 . Bulk RNA-seq library preparation and sequencing The NEBNext Ultra II Directional Library Prep protocol (NEB #E7760S) was used with NEB’s bacterial rRNA depletion kit (NEB # E7850S) to make bulk RNA-seq libraries for whole transcriptome sequencing. 100 ng RNA input was used and adaptors were diluted 25-fold before ligating to the cDNA. The same set of RNA samples was used in a hybrid workflow combining pre-amplification with the NEBNext Ultra II Library Prep. 10 ng of total RNA was subjected to rRNA depletion and then cDNA synthesis and amplification using the Single Primer Isothermal Amplification (SPIA) method with the Crescendo cDNA synthesis for qPCR kit (Tecan Genomics, Inc.). For these samples, RNA was not fragmented prior to cDNA synthesis. After SPIA amplification, cDNA was fragmented using components of the Allegro Targeted Genotyping V2 kit (Tecan Genomics, Inc.) before proceeding with end repair, adaptor ligation (25-fold diluted adaptors) and PCR-enrichment using the NEBNext Ultra II Directional Library Prep kit components. E. coli RNA was included as a positive control in both the workflows. Index PCR was performed using the NEBNext Multiplex Oligos for Illumina (Dual Index Primers Set 1) (NEB #E7600S). The final library purification was carried out using 0.78x Ampure XP beads to remove the adaptor peak. Libraries were assessed on the TapeStation and Qubit dsDNA high sensitivity assay before normalizing to 4 nM. cDNA synthesis and SPIA amplification First and second strand cDNA synthesis were performed using the Crescendo cDNA synthesis for qPCR protocol (Tecan Genomics, Inc.). cDNA samples were then divided, with one portion subjected to SPIA amplification per the Crescendo protocol. cDNA was purified with AMPure XP beads at 1.8x and eluted in 33 ul of 1x TE. The resulting cDNA samples were quantified using NanoDrop following the kit protocol and Qubit dsDNA high sensitivity assays. The samples that were amplified with SPIA yielded >1 ug of cDNA and the non-SPIA samples were 6-8 ng. Targeted RNA-seq library preparation and sequencing using single-primer extension probes SPIA-amplified cDNA samples were diluted 20-fold and 10-20 ng of cDNA was used as input for the Allegro Targeted Genotyping V2 protocol (Tecan Genomics, Inc.) with the custom probe pool. Non-amplified cDNA was not diluted and ∼1 ng was used as input for the Allegro protocol. Genomic DNA was used at 10-50 ng input. Each sample was enzymatically fragmented, followed by ligation of barcoded adaptors. Barcoded samples were then purified, pooled, and placed in an overnight hybridization reaction mixture with the probe pool. The following day (>12h) the DNA polymerase enzyme was added to the reaction for extension at 72°C for 10 min. Post-enrichment purification was done with 0.8x AMPure XP beads. A qPCR step was used to determine the number of cycles for library amplification (11 cycles). The final library pool was bead purified with 0.8x AMPure XP beads. A low template control (LTC) with 50 pg of S. epidermidis RNA input and a mouse stool metagenome DNA sample were used as sensitivity and specificity controls respectively. The library pool was adjusted to 32 nM and 20 ul of the pool was used for sequencing (20 fmole/sample). The final sequencing pool also comprised libraries created using the NEBNext Ultra II Library prep protocol and Illumina DNA Prep (M) kit that were normalized to 4 nM and 0.2 nM respectively to represent 20 fmoles for the NEBNext samples and 1 fmole for the Illumina DNA Prep libraries. The exception was the E. coli RNA positive control (100 ng input) that was pooled at a lower amount to represent 5 fmoles in the sequencing pool. The library pool was loaded on NovaSeq 6000 SP flowcell 2×100bp run. The custom read 1 (R1) primer provided in the Allegro kit was spiked into the Illumina read 1 primer position at 0.3 uM and a phiX174 control library was spiked-in at 1% as recommended. Targeted RNA-seq library preparation and sequencing using molecular inversion probes Targeted RNA libraries were prepared with the Low Input DNA Target Capture kit (Molecular Loop Biosciences, Inc.) using the cDNA generated with the Crescendo kit (Tecan Genomics, Inc.), including both the SPIA and non-SPIA pre-amplified samples, as well as the gDNA samples. The sample input was not normalized (50 pg – 50 ng) to evaluate its effect on probe hybridization efficiencies and consequently the number of sequencing reads generated from each of these samples. Among the 32 samples, only the human blood high molecular weight genomic DNA sample was pre-treated by heating it to 95°C for 5 min for denaturation. The protocol was followed using the kit user guide and the custom probe pool was hybridized for 18 hours (recommended ≥16 hours). After hybridization, each library was amplified by PCR for 20 cycles, pooled at equal volume (10 ul each), and the library pool was purified with 0.8x Agencourt RNAXP Clean beads and eluted in 25ul of 1xTE. The library pool was quantified with the Qubit high sensitivity dsDNA assay and on the TapeStation D1000 high-sensitivity ScreenTape, which resulted in a single peak at ∼415 bp. The library pool was loaded on a NovaSeq 6000 SP flowcell 2×100bp run. The custom workflow was selected for the sequencing run using custom read1, read2 and sequencing primers provided by Molecular Loop. The phiX174 control library was spiked-in at 1%. Illumina DNA Prep Libraries Libraries of Se and Sa gDNA were prepared using the Illumina DNA Prep, Tagmentation kit (Illumina, 20018705) and indexed using IDT for Illumina DNA/RNA UD Indexes (Illumina, 20042666). Reads were assembled using CLC Genomics Workbench and the resulting assembly was compared to the reference sequences for the Se and Sa genomes, confirming that the cultured cells represented the expected genomes. Genome sequence alignment Reads were processed with trimmomatic 0.39 ( 41 ) to remove low-quality reads. For bulk RNA and SPE reads, the adapters were removed from the fastq reads using Cutadapt ( 42 ) version 4.4s, using the bait sequence ‘AGATCGGAAGAG’. For MIP reads, seqtk v1.4-r122 was used ( https://github.com/lh3/seqtk ) to remove the five bases from the 5′-end of the sequences as seqtk trimfq-b 5 ˂fastq˃ . Reads were aligned to reference genomes Sa m2872 (GCF_017329165.1) and Se ATCC 14990 (GCF_006094375.1) as well as Sa USA300 (GCF_002993865.1) Se Tu3298 ( https://github.com/ohlab/S.epi-CRISPRi-and-RNA-Seq ) using BWA-MEM ( 43 ) version 0.7.17-r1188 as bwa mem-t 8-k 19-w 100. The mapped reads of each of the Se and Sa species were extracted with bamtools v2.5.2 ( 44 ) with parameter -isMapped ‘true’ and subsequently filtered with MAPQ >30 and edit distance NM <4. High-quality sequences were sorted and converted into.bam and.bed files for downstream processing by SAMtools ( 45 ) v0.7.17-r1188. For bulk RNA-seq, the read count aligned to each CDS region was computed using featureCounts v1.6.4( 46 ) (from Subread1.6.4) with-t CDS or-t rRNA, as appropriate, and summarized as counts per CDS per million reads per kilobase CDS length (RPKM). Probe sequence alignment We also used BWA-MEM to align the SPE and MIP reads to a database of the probe sequences. For MIP, we used the seqinr R package ( 47 ) to extract the binding locations consisting of the first and the last 40bps of the probe sequences and generated new fasta files with the ‘first 40bps’ and ‘last 40bps’ probe data. We aligned the reads of each sample separately to the probe sequences of each species. The mapped reads were extracted as before with bamtools v2.5.2 and subsequently filtered with MAPQ >30 and NM 30 and NM <3 in MIP. The high-quality sequences were sorted and converted into bam and bed files for downstream processing by SAMtools v0.7.17-r1188. The raw counts were generated with bedtools coverage for each sample from the probe sequences (.bed) and cleaned read (.bam) files by bedtools v2.31.0 ( 48 ). The raw counts were CPM normalized as where denotes the number of reads mapped to location i in sample j and species S , S ∈ { S e, S a} and R j,s is the total number of mapped reads in sample j and species s . Probe count tables are provided as Supplemental Tables 6-10 . Differential Expression Analysis and Correlation Analyses Differential expression analysis across stress conditions was performed in Rv4.4.1 ( 49 ) using DESeq2 ( 50 ) using a two factor design matrix accounting for stress treatment (TSB, acid, or urea) and SPIA status including interaction terms. In all comparisons, differentially expressed genes were defined as those with fold-change magnitude > 4 and adjusted p-value < 0.01. Correlation matrices were calculated in R as Euclidean distances using the stats package ( 49 ) and pairwise correlations were assessed via Pearson correlation coefficients (R values) of best fit linear models to two-dimensional data using ggpubr ( 51 ). Data availability All data used in these analyses are available at the NCBI Gene Expression Omnibus under accession GSE279187. Author Contributions GD and MA performed data analysis and wrote the manuscript. SC wrote the manuscript. PS performed the targeted sequencing experiments. IB and EM contributed to data analysis. EA performed the RHE experiments. JO and MA conceived the study. Acknowledgements We thank the Genome Technologies Scientific Service for performing sequence data collection and Mitch Kostich for helpful discussions regarding data analysis. References 1. ↵ Huang J , Jiang Z , Wang Y , Fan X , Cai J , Yao X , Liu L , Huang J , He J , Xie C , Wu Q , Cao Y , Leung EL. 2020 . Modulation of gut microbiota to overcome resistance to immune checkpoint blockade in cancer immunotherapy . Curr Opin Pharmacol , 2020/07/04 ed. 54 : 1 – 10 . OpenUrl CrossRef PubMed 2. ↵ van den Berg FF , van Dalen D , Hyoju SK , van Santvoort HC , Besselink MG , Wiersinga WJ , Zaborina O , Boermeester MA , Alverdy J . 2021 . Western-type diet infiuences mortality from necrotising pancreatitis and demonstrates a central role for butyrate . Gut , 20200901st ed. 70 : 915 – 927 . OpenUrl Abstract / FREE Full Text 3. ↵ Czaran T , Hoekstra RF. 2009 . Microbial communication, cooperation and cheating: quorum sensing drives the evolution of cooperation in bacteria . PLoS One , 2009/08/18 ed. 4 : e6655 . OpenUrl CrossRef PubMed 4. Brenner K , Karig DK , Weiss R , Arnold FH. 2007 . Engineered bidirectional communication mediates a consensus in a microbial biofilm consortium . Proc Natl Acad Sci U A , 2007/10/26 ed. 104 : 17300 – 4 . OpenUrl Abstract / FREE Full Text 5. Franzosa EA , McIver LJ , Rahnavard G , Thompson LR , Schirmer M , Weingart G , Lipson KS , Knight R , Caporaso JG , Segata N , Huttenhower C. 2018 . Species-level functional profiling of metagenomes and metatranscriptomes . Nat Methods , 2018/11/01 ed. 15 : 962 – 968 . OpenUrl CrossRef PubMed 6. ↵ Peters BA , Wilson M , Moran U , Pavlick A , Izsak A , Wechter T , Weber JS , Osman I , Ahn J. 2019 . Relating the gut metagenome and metatranscriptome to immunotherapy responses in melanoma patients . Genome Med , 2019/10/11 ed. 11 : 61 . OpenUrl CrossRef PubMed 7. ↵ Korry BJ , Cabral DJ , Belenky P . 2020 . Metatranscriptomics Reveals Antibiotic-Induced Resistance Gene Expression in the Murine Gut Microbiota . Front Microbiol 11 : 322 . OpenUrl CrossRef PubMed 8. ↵ Kang D , Shi B , Erfe MC , Craft N , Li H . 2015 . Vitamin B12 modulates the transcriptome of the skin microbiota in acne pathogenesis . Sci Transl Med 7 : 293r a103 . OpenUrl 9. ↵ Abu-Ali GS , Mehta RS , Lloyd-Price J , Mallick H , Branck T , Ivey KL , Drew DA , DuLong C , Rimm E , Izard J , Chan AT , Huttenhower C . 2018 . Metatranscriptome of human faecal microbial communities in a cohort of adult men . Nat Microbiol , 20180115th ed. 3:356–366. 10. ↵ Jousset A , Bienhold C , Chatzinotas A , Gallien L , Gobet A , Kurm V , Kusel K , Rillig MC , Rivett DW , Salles JF , van der Heijden MG , Youssef NH , Zhang X , Wei Z , Hol WH. 2017 . Where less may be more: how the rare biosphere pulls ecosystems strings . ISME J , 2017/01/11 ed. 11 : 853 – 862 . OpenUrl CrossRef PubMed 11. Arumugam M , Raes J , Pelletier E , Le Paslier D , Yamada T , Mende DR , Fernandes GR , Tap J , Bruls T , Batto JM , Bertalan M , Borruel N , Casellas F , Fernandez L , Gautier L , Hansen T , Hattori M , Hayashi T , Kleerebezem M , Kurokawa K , Leclerc M , Levenez F , Manichanh C , Nielsen HB , Nielsen T , Pons N , Poulain J , Qin J , Sicheritz-Ponten T , Tims S , Torrents D , Ugarte E , Zoetendal EG , Wang J , Guarner F , Pedersen O , de Vos WM , Brunak S , Dore J , Meta HITC , Antolin M , Artiguenave F , Blottiere HM , Almeida M , Brechot C , Cara C , Chervaux C , Cultrone A , Delorme C , Denariaz G , Dervyn R , Foerstner KU , Friss C , van de Guchte M , Guedon E , Haimet F , Huber W , van Hylckama-Vlieg J , Jamet A , Juste C , Kaci G , Knol J , Lakhdari O , Layec S , Le Roux K , Maguin E , Merieux A , Melo Minardi R , M’Rini C , Muller J , Oozeer R , Parkhill J , Renault P , Rescigno M , Sanchez N , Sunagawa S , Torrejon A , Turner K , Vandemeulebrouck G , Varela E , Winogradsky Y , Zeller G , Weissenbach J , Ehrlich SD , Bork P. 2011 . Enterotypes of the human gut microbiome . Nature , 2011/04/22 ed. 473 : 174 – 80 . OpenUrl CrossRef PubMed Web of Science 12. ↵ Claussen JC , Skieceviciene J , Wang J , Rausch P , Karlsen TH , Lieb W , Baines JF , Franke A , Hutt MT. 2017 . Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome . PLoS Comput Biol , 2017/06/24 ed. 13 : e1005361 . OpenUrl CrossRef PubMed 13. ↵ Human Microbiome Project C . 2012 . Structure, function and diversity of the healthy human microbiome . Nature , 2012/06/16 ed. 486 : 207 – 14 . OpenUrl CrossRef PubMed Web of Science 14. ↵ Iwase T , Uehara Y , Shinji H , Tajima A , Seo H , Takada K , Agata T , Mizunoe Y. 2010 . Staphylococcus epidermidis Esp inhibits Staphylococcus aureus biofilm formation and nasal colonization . Nature , 2010/05/21 ed. 465 : 346 – 9 . OpenUrl CrossRef PubMed Web of Science 15. Uberoi A , Bartow-McKenney C , Zheng Q , Flowers L , Campbell A , Knight SAB , Chan N , Wei M , Lovins V , Bugayev J , Horwinski J , Bradley C , Meyer J , Crumrine D , Sutter CH , Elias P , Mauldin E , Sutter TR , Grice EA . 2021 . Commensal microbiota regulates skin barrier function and repair via signaling through the aryl hydrocarbon receptor . Cell Host Microbe , 20210701st ed. 29:1235-1248 e8. 16. Byrd AL , Deming C , Cassidy SKB , Harrison OJ , Ng WI , Conlan S , Program NCS , Belkaid Y , Segre JA , Kong HH. 2017 . Staphylococcus aureus and Staphylococcus epidermidis strain diversity underlying pediatric atopic dermatitis . Sci Transl Med , 2017/07/07 ed. 9. 17. ↵ Zhou W , Spoto M , Hardy R , Guan C , Fleming E , Larson PJ , Brown JS , Oh J . 2020 . Host-Specific Evolutionary and Transmission Dynamics Shape the Functional Diversification of Staphylococcus epidermidis in Human Skin . Cell 180 : 454 – 470 .e18. OpenUrl CrossRef PubMed 18. ↵ Bernstein JA , Khodursky AB , Lin PH , Lin-Chao S , Cohen SN. 2002 . Global analysis of mRNA decay and abundance in Escherichia coli at single-gene resolution using two-color fiuorescent DNA microarrays . Proc Natl Acad Sci U A , 2002/07/18 ed. 99 : 9697 – 702 . OpenUrl Abstract / FREE Full Text 19. Wu L , Liu X , Schadt CW , Zhou J. 2006 . Microarray-based analysis of subnanogram quantities of microbial community DNAs by using whole-community genome amplification . Appl Env Microbiol , 2006/07/06 ed. 72 : 4931 – 41 . OpenUrl Abstract / FREE Full Text 20. ↵ Gardner SN , Jaing CJ , McLoughlin KS , Slezak TR. 2010 . A microbial detection array (MDA) for viral and bacterial detection . BMC Genomics , 2010/11/27 ed. 11 : 668 . OpenUrl CrossRef PubMed 21. ↵ Samorodnitsky E , Datta J , Jewell BM , Hagopian R , Miya J , Wing MR , Damodaran S , Lippus JM , Reeser JW , Bhatt D , Timmers CD , Roychowdhury S. 2015 . Comparison of custom capture for targeted next-generation DNA sequencing . J Mol Diagn , 2014/12/22 ed. 17 : 64 – 75 . OpenUrl CrossRef PubMed 22. Gnirke A , Melnikov A , Maguire J , Rogov P , LeProust EM , Brockman W , Fennell T , Giannoukos G , Fisher S , Russ C , Gabriel S , Jaffe DB , Lander ES , Nusbaum C. 2009 . Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing . Nat Biotechnol , 2009/02/03 ed. 27 : 182 – 9 . OpenUrl CrossRef PubMed Web of Science 23. Depledge DP , Palser AL , Watson SJ , Lai IY , Gray ER , Grant P , Kanda RK , Leproust E , Kellam P , Breuer J. 2011 . Specific capture and whole-genome sequencing of viruses from clinical samples . PLoS One , 2011/11/30 ed. 6 : e27805 . OpenUrl CrossRef PubMed 24. ↵ Nimmo C , Shaw LP , Doyle R , Williams R , Brien K , Burgess C , Breuer J , Balloux F , Pym AS. 2019 . Whole genome sequencing Mycobacterium tuberculosis directly from sputum identifies more genetic diversity than sequencing from culture . BMC Genomics , 2019/05/22 ed. 20 : 389 . OpenUrl CrossRef PubMed 25. ↵ Benjamino J , Leopold B , Phillips D , Adams MD . 2021 . Genome-Based Targeted Sequencing as a Reproducible Microbial Community Profiling Assay . mSphere 6 : doi: 10.1128/msphere.01325-20 . OpenUrl CrossRef 26. ↵ Oh J , Byrd AL , Park M , Kong HH , Segre JA . 2016 . Temporal Stability of the Human Skin Microbiome . Cell 165 : 854 – 866 . OpenUrl CrossRef PubMed 27. Grice EA , Segre JA . 2011 . The skin microbiome . Nat Rev Microbiol 9 : 244 – 253 . OpenUrl CrossRef PubMed 28. ↵ Agostinetto G , Bozzi D , Porro D , Casiraghi M , Labra M , Bruno A . 2021 . SKIOME Project: a curated collection of skin microbiome datasets enriched with study-related metadata . bioRxiv doi: 10.1101/2021.08.17.456635 . OpenUrl Abstract / FREE Full Text 29. ↵ Hardenbol P , Banér J , Jain M , Nilsson M , Namsaraev EA , Karlin-Neumann GA , Fakhrai-Rad H , Ronaghi M , Willis TD , Landegren U , Davis RW . 2003 . Multiplexed genotyping with sequence-tagged molecular inversion probes . Nat Biotechnol 21 : 673 – 678 . OpenUrl CrossRef PubMed Web of Science 30. ↵ Myrmel M , Oma V , Khatri M , Hansen HH , Stokstad M , Berg M , Blomström A-L . 2017 . Single primer isothermal amplification (SPIA) combined with next generation sequencing provides complete bovine coronavirus genome coverage and higher sequence depth compared to sequence-independent single primer amplification (SISPA) . PLOS ONE 12 : e0187780 . OpenUrl CrossRef PubMed 31. ↵ Korem T , Zeevi D , Suez J , Weinberger A , Avnit-Sagi T , Pompan-Lotan M , Matot E , Jona G , Harmelin A , Cohen N , Sirota-Madi A , Thaiss CA , Pevsner-Fischer M , Sorek R , Xavier R , Elinav E , Segal E . 2015 . Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples . Science 349 : 1101 – 1106 . OpenUrl Abstract / FREE Full Text 32. ↵ Grether-Beck S , Felsner I , Brenden H , Kohne Z , Majora M , Marini A , Jaenicke T , Rodriguez-Martin M , Trullas C , Hupe M , Elias PM , Krutmann J . 2012 . Urea Uptake Enhances Barrier Function and Antimicrobial Defense in Humans by Regulating Epidermal Gene Expression . J Invest Dermatol 132 : 1561 – 1572 . OpenUrl CrossRef PubMed Web of Science 33. ↵ Zhou C , Bhinderwala F , Lehman MK , Thomas VC , Chaudhari SS , Yamada KJ , Foster KW , Powers R , Kielian T , Fey PD . 2019 . Urease is an essential component of the acid response network of Staphylococcus aureus and is required for a persistent murine kidney infection . PLoS Pathog 15 : e1007538 . OpenUrl CrossRef PubMed 34. ↵ Mager LF , Burkhard R , Pett N , Cooke NCA , Brown K , Ramay H , Paik S , Stagg J , Groves RA , Gallo M , Lewis IA , Geuking MB , McCoy KD. 2020 . Microbiome-derived inosine modulates response to checkpoint inhibitor immunotherapy . Science , 2020/08/15 ed. 369 : 1481 – 1489 . OpenUrl Abstract / FREE Full Text 35. Patnode ML , Beller ZW , Han ND , Cheng J , Peters SL , Terrapon N , Henrissat B , Le Gall S , Saulnier L , Hayashi DK , Meynier A , Vinoy S , Giannone RJ , Hettich RL , Gordon JI. 2019 . Interspecies Competition Impacts Targeted Manipulation of Human Gut Bacteria by Fiber-Derived Glycans . Cell , 2019/09/21 ed. 179 : 59 – 73 e13 . OpenUrl CrossRef PubMed 36. Fischer CN , Trautman EP , Crawford JM , Stabb EV , Handelsman J , Broderick NA. 2017 . Metabolite exchange between microbiome members produces compounds that infiuence Drosophila behavior . Elife , 2017/01/10 ed. 6. 37. ↵ Kim SG , Becattini S , Moody TU , Shliaha PV , Littmann ER , Seok R , Gjonbalaj M , Eaton V , Fontana E , Amoretti L , Wright R , Caballero S , Wang ZX , Jung HJ , Morjaria SM , Leiner IM , Qin W , Ramos R , Cross JR , Narushima S , Honda K , Peled JU , Hendrickson RC , Taur Y , van den Brink MRM , Pamer EG . 2019 . Microbiota-derived lantibiotic restores resistance against vancomycin-resistant Enterococcus . Nature , 2019/08/23 ed. 572 : 665 – 669 . OpenUrl CrossRef PubMed 38. ↵ Spoto M , Riera Puma JP , Fleming E , Guan C , Ondouah Nzutchi Y , Kim D , Oh J . 2022 . Large-Scale CRISPRi and Transcriptomics of Staphylococcus epidermidis Identify Genetic Factors Implicated in Lifestyle Versatility . mBio 0 : e02632 – 22 . OpenUrl 39. ↵ Long LL , Svenson KL , Mourino AJ , Michaud M , Fahey JR , Waterman L , Vandegrift KL , Adams MD . 2021 . Shared and distinctive features of the gut microbiome of C57BL/6 mice from different vendors and production sites, and in response to a new vivarium . Lab Anim 50 : 185 – 195 . OpenUrl CrossRef 40. ↵ Page AJ , Cummins CA , Hunt M , Wong VK , Reuter S , Holden MTG , Fookes M , Falush D , Keane JA , Parkhill J . 2015 . Roary: rapid large-scale prokaryote pan genome analysis . Bioinforma Oxf Engl 31 : 3691 – 3693 . OpenUrl 41. ↵ Bolger AM , Lohse M , Usadel B . 2014 . Trimmomatic: a fiexible trimmer for Illumina sequence data . Bioinformatics 30 : 2114 – 2120 . OpenUrl CrossRef PubMed Web of Science 42. ↵ Martin M . 2011 . Cutadapt removes adapter sequences from high-throughput sequencing reads. 1 . EMBnet.journal 17 : 10 – 12 . OpenUrl CrossRef 43. ↵ Li H. 2013 . Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM . ArXiv 1303 . 44. ↵ Barnett D , Garrison E , Quinlan A , Strömberg M , Marth G . 2011 . BamTools: a C++ API and toolkit for analyzing and managing BAM files . Bioinformatics 27 . 45. ↵ Li H , Handsaker B , Wysoker A , Fennell T , Ruan J , Homer N , Marth G , Abecasis G , Durbin R . 2009 . The Sequence Alignment/Map format and SAMtools . Bioinformatics 25 : 2078 – 2079 . OpenUrl CrossRef PubMed Web of Science 46. ↵ Liao Y , Smyth GK , Shi W . 2014 . featureCounts: an efficient general purpose program for assigning sequence reads to genomic features . Bioinforma Oxf Engl 30 : 923 – 930 . OpenUrl 47. ↵ Gouy M , Milleret F , Mugnier C , Jacobzone M , Gautier C . 1984 . ACNUC: a nucleic acid sequence data base and analysis system . Nucleic Acids Res 12 : 121 – 127 . OpenUrl CrossRef PubMed 48. ↵ Quinlan AR , Hall IM . 2010 . BEDTools: a fiexible suite of utilities for comparing genomic features . Bioinforma Oxf Engl 26 : 841 – 842 . OpenUrl 49. ↵ R Core Team . 2024 . R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing , Vienna, Austria . https://www.R-project.org/ . 50. ↵ Love MI , Huber W , Anders S . 2014 . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 . Genome Biol 15 : 550 . OpenUrl CrossRef PubMed 51. ↵ Kassambara A. 2016 . ggpubr: “ggplot2” Based Publication Ready Plots . View the discussion thread. Back to top Previous Next Posted December 01, 2024. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following TEAL-seq: Targeted Expression Analysis Sequencing Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. 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