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PurposeThere is uncertainty around the safety of SSRIs for treating depression during pregnancy. We aimed 1) to address confounding by indication, as well as socioeconomic and environmental factors associated with depression and 2) evaluate associations of timing of SSRI exposure in pregnancy with the risk of preterm birth and related outcomes (small for gestational age and low birthweight) among women with depression before pregnancy.MethodsWe conducted propensity score-adjusted regression to calculate odds ratios (OR) of preterm birth, small for gestational age, and low birth weight. We accounted for maternal/pregnancy characteristics, pre-pregnancy comorbidity/depression severity, social vulnerability, rural health disparity, and pre-natal depression severity. We additionally conducted a drug-specific analysis and assessed the impact of other classes of antidepressants within our cohort of interest.ResultsAmong women with a history of depression, we identified women with indication of depression ≤ 180 days before pregnancy (n=6,408). Women with no SSRI order during pregnancy (n=3,122) constituted the unexposed group (no SSRI exposure group). The late SSRI exposure group consisted of women with an SSRI order after the first trimester (n=2,596). The early-only SSRI exposure group consisted of women with SSRI orders only in the first trimester (n=691). Late SSRI exposure group had an increased risk of preterm birth of OR=1.7 ([1.3,2.2], pConclusionsThese findings suggest associations between preterm birth/low birthweight and SSRI exposure is dependent on exposure timing during pregnancy. Small for gestational age is not associated with SSRI exposure.

Variation in the blood metabolome is intimately related to human health. Prior work has shown that host genetics and gut microbiome composition, combined, explain sizable, but orthogonal, components of the overall variance in blood metabolomic profiles. However, few details are known about the interplay between genetics and the microbiome in explaining variation on a metabolite-by-metabolite level. Here, we performed analyses of variance for each of the 945 blood metabolites that were robustly detected across a cohort of 2,049 individuals, while controlling for a number of relevant covariates, like sex, age, and genetic ancestry. Over 60% of the detected blood metabolites were significantly associated with either host genetics or the gut microbiome, with more than half of these associations driven solely by the microbiome and around 30% under hybrid genetic-microbiome control. The variances explained by genetics and the microbiome for each metabolite were indeed largely additive, although subtle, but significant, non-additivity was detected. We found that interaction effects, where a metabolitemicrobe association was specific to a particular genetic background, were quite common, albeit with modest effect sizes. The outputs of our integrated genetic-microbiome regression models provide novel biological insights into the processes governing the composition of the blood metabolome. For example, we found that unconjugated secondary bile acids were solely associated with the microbiome, while their conjugated forms were under strong host genetic control. Overall, our results reveal which components of the blood metabolome are under strong genetic control, which are more dependent on gut microbiome composition, and which are dependent upon both. This knowledge will help to guide targeted interventions designed to alter the composition of the blood metabolome.

Neurodegenerative diseases (NDDs) encompass a wide range of conditions that arise owing to progressive degeneration and the ultimate loss of nerve cells in the brain and peripheral nervous system. NDDs such as Alzheimer's, Parkinson's, and Huntington's diseases negatively impact both length and quality of life, due to lack of effective disease-modifying treatments. Herein, we review the use of genome-scale metabolic models, network-based approaches, and integration with multiomics data to identify key biological processes that characterize NDDs. We describe powerful systems biology approaches for modeling NDD pathophysiology by leveraging in silico models that are informed by patient-derived multiomics data. These approaches can enable mechanistic insights into NDD-specific metabolic dysregulations that can be leveraged to identify potential metabolic markers of disease and predisease states.

IntroductionComprehensive treatment of Alzheimer’s disease and related dementias (ADRD) requires not only pharmacologic treatment but also management of existing medical conditions and lifestyle modifications including diet, cognitive training, and exercise. The Coaching for Cognition in Alzheimer’s (COCOA) trial was a prospective randomized controlled trial (RCT) to test the hypothesis that a remotely coached multimodal lifestyle intervention would improve early-stage Alzheimer’s disease (AD). AD results from the interplay of multiple interacting dysfunctional biological systems. Specific causes of AD differ between individuals. Personalized, multimodal therapies are needed to best prevent and treat AD. COCOA collected psychometric, clinical, lifestyle, genomic, proteomic, metabolomic and microbiome data at multiple timepoints across two years for each participant. These data enable systems-biology analyses. We report analyses of the first COCOA data freeze. This analysis includes an evaluation of the effect of the intervention on outcome measures. It also includes systems analyses to identify molecular mediators that convey the effect of personalized multimodal lifestyle interventions on amelioration of cognitive trajectory.MethodsA total of 55 participants with early-stage AD from Southern California were randomized into two parallel arms. Arm 1 (control; N=24) received standard of care. Arm 2 (intervention; N=31) also received telephonic personalized coaching for multiple lifestyle interventions including diet, exercise, and cognitive training. COCOA’s overarching aim was to gather dense molecular data from an AD cohort to improve understanding of pathophysiology and advance treatment. For the RCT, COCOA’s objective was to test the hypothesis that the Memory Performance Index (MPI) trajectory would be better in the intervention arm than in the control arm. The Functional Assessment Staging Test (FAST) was assessed for a secondary outcome. Assessments were blinded. The nature of the intervention precluded participant blinding.ResultsThe intervention arm ameliorated 2.6 ± 0.8 MPI points (p = 0.0007; N = 48) compared to the control arm over the two-year intervention. Top-ranked candidate mediators included: albumin, propionylcarnitine, sphingomyelin, hexadecanedioate, acetylkynurenine, tiglylcarnitine, IL18R1, palmitoyl-sphingosine-phosphoethanolamine, acetyltryptophan, and IL17D. These individual molecules implicated inflammatory and nitrogen/tryptophan metabolism pathways. No important adverse events or side effects were observed.ConclusionsClinical trials should include frequent assessment of dense data to maximize knowledge gained. Such knowledge is useful not only in testing a primary hypothesis, but also in advancing basic biological and pathophysiological knowledge, understanding mechanisms explaining trial results, generating synergistic knowledge tangential to preconceived hypotheses, and refining interventions for clinical translation. Data from every trial should allow an intervention to be refined and then tested in future trials, driving iterative improvement. Multimodal lifestyle interventions are effective for ameliorating cognitive decline and may have an effect size larger than pharmacological interventions. Effects may be molecularly idiosyncratic; personalization of interventions is important. Dietary changes and exercise are likely to be beneficial components of multimodal interventions in many individuals. Remote coaching is an effective intervention for early stage ADRD. Remote interventions were effective during the COVID pandemic.

Gene regulation is essential to placental function and fetal development. We report a genome-scale transcriptional regulatory network (TRN) of the human placenta built using digital genomic footprinting and transcriptomic data. We integrated 475 transcriptomes and 12 DNase hypersensitivity datasets from placental samples to globally and quantitatively map transcription factor (TF)-target gene interactions. In an independent dataset, the TRN model predicted target gene expression with an out of sample R2 value greater than 0.25 for 74% of target genes. We performed siRNA knockdowns of 4 TFs and achieved concordance between the predicted gene targets in our TRN and differences in expression of knockdowns with an accuracy of >0.7 for 3 of the 4 TFs. Our final model contained 113,158 interactions across 391 TFs and 7,712 target genes and is publicly available. We identified six TFs which were significantly enriched as regulators for genes previously associated with preterm birth.

With the goal of identifying metabolites that significantly correlate with the protective e2 allele of the apolipoprotein E (APOE) gene, we established a consortium of five studies of healthy aging and extreme human longevity with 3545 participants. This consortium includes the New England Centenarian Study, the Baltimore Longitudinal Study of Aging, the Arivale study, the Longevity Genes Project/LonGenity studies, and the Long Life Family Study. We analyzed the association between APOE genotype groups E2 (e2e2 and e2e3 genotypes, N = 544), E3 (e3e3 genotypes, N = 2299), and E4 (e3e4 and e4e4 genotypes, N = 702) with metabolite profiles in the five studies and used fixed effect meta-analysis to aggregate the results. Our meta-analysis identified a signature of 19 metabolites that are significantly associated with the E2 genotype group at FDR

Aging manifests as progressive deterioration in cellular and systemic homeostasis, requiring systems-level perspectives to understand the gradual molecular dysregulation of underlying biological processes. Here, we report systems-level changes in the molecular regulation of biological processes under multiple lifespan-extending interventions in mice and across age in humans. In mouse cohorts, Differential Rank Conservation (DIRAC) analyses of liver proteomics and transcriptomics show that mechanistically distinct prolongevity interventions tighten the regulation of aging-related biological modules, including fatty acid metabolism and inflammation processes. An integrated analysis of liver transcriptomics with mouse genome-scale metabolic model supports the shifts in fatty acid metabolism. Additionally, the difference in DIRAC patterns between proteins and transcripts suggests biological modules which may be tightly regulated via cap-independent translation. In a human cohort spanning the majority of the adult lifespan, DIRAC analyses of blood proteomics and metabolomics demonstrate that regulation of biological modules does not monotonically loosen with age; instead, the regulatory patterns shift according to both chronological and biological ages. Our findings highlight the power of systems-level approaches to identifying and characterizing the biological processes involved in aging and longevity.

The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.

Dysregulation of sphingomyelin and ceramide metabolism have been implicated in Alzheimer’s disease. Genome-wide and transcriptome-wide association studies have identified various genes and genetic variants in lipid metabolism that are associated with Alzheimer’s disease. However, the molecular mechanisms of sphingomyelin and ceramide disruption remain to be determined. We focus on the sphingolipid pathway and carry out multi-omics analyses to identify central and peripheral metabolic changes in Alzheimer’s patients, correlating them to imaging features. Our multi-omics approach is based on (a) 2114 human post-mortem brain transcriptomics to identify differentially expressed genes; (b) in silico metabolic flux analysis on context-specific metabolic networks identified differential reaction fluxes; (c) multimodal neuroimaging analysis on 1576 participants to associate genetic variants in sphingomyelin pathway with Alzheimer’s disease pathogenesis; (d) plasma metabolomic and lipidomic analysis to identify associations of lipid species with dysregulation in Alzheimer’s; and (e) metabolite genome-wide association studies to define receptors within the pathway as a potential drug target. We validate our hypothesis in amyloidogenic APP/PS1 mice and show prolonged exposure to fingolimod alleviated synaptic plasticity and cognitive impairment in mice. Our integrative multi-omics approach identifies potential targets in the sphingomyelin pathway and suggests modulators of S1P metabolism as possible candidates for Alzheimer’s disease treatment.

A better understanding of the metabolic alterations in immune cells during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may elucidate the wide diversity of clinical symptoms experienced by individuals with coronavirus disease 2019 (COVID-19). Here, we report the metabolic changes associated with the peripheral immune response of 198 individuals with COVID-19 through an integrated analysis of plasma metabolite and protein levels as well as single-cell multiomics analyses from serial blood draws collected during the first week after clinical diagnosis. We document the emergence of rare but metabolically dominant T cell subpopulations and find that increasing disease severity correlates with a bifurcation of monocytes into two metabolically distinct subsets. This integrated analysis reveals a robust interplay between plasma metabolites and cell-type-specific metabolic reprogramming networks that is associated with disease severity and could predict survival.

Dysregulation of sphingomyelin (SM) and ceramide metabolism have been implicated in Alzheimer’s Disease (AD). Genome-wide and transcriptome wide association studies have identified various genes and genetic variants in lipid metabolism that are associated with AD. However, the molecular mechanisms of sphingomyelin and ceramide disruption remain to be determined. Evaluation of peripheral lipidomic profiles is useful in providing perspective on metabolic dysregulation in preclinical and clinical AD states. In this study, we focused on the sphingolipid pathway and carried out multi-omic analyses to identify central and peripheral metabolic changes in AD patients and correlate them to imaging features and cognitive performance in amyloidogenic mouse models. Our multi-omic approach was based on (a) 2114 human post-mortem brain transcriptomics to identify differentially expressed genes; (b) in silico metabolic flux analysis on 1708 context-specific metabolic networks to identify differential reaction fluxes; (c) multimodal neuroimaging analysis on 1576 participants to associate genetic variants in SM pathway with AD pathogenesis; (d) plasma metabolomic and lipidomic analysis to identify associations of lipid species with dysregulation in AD; (e) metabolite genome-wide association studies (mGWAS) to define receptors within pathway as potential drug target. Our findings from complementary approaches suggested that depletion of S1P compensated for AD cellular pathology, likely by upregulating the SM pathway, suggesting that modulation of S1P signaling may have protective effects in AD. We tested this hypothesis in APP/PS1 mice and showed that prolonged exposure to fingolimod, an S1P signaling modulator approved for treatment of multiple sclerosis, alleviated the cognitive impairment in mice. Our multi-omic approach identified potential targets in the SM pathway and suggested modulators of S1P metabolism as possible candidates for AD treatment.

Longitudinal multi-omics measurements are highly valuable in studying heterogeneity in health and disease phenotypes. For thousands of people, we have collected longitudinal multi-omics data. To analyze, interpret and visualize this extremely high-dimensional data, we use the Pareto Task Inference (ParTI) method. We find that the clinical labs data fall within a tetrahedron. We then use all other data types to characterize the four archetypes. We find that the tetrahedron comprises three wellness states, defining a wellness triangular plane, and one aberrant health state that captures aspects of commonality in movement away from wellness. We reveal the tradeoffs that shape the data and their hierarchy, and use longitudinal data to observe individual trajectories. We then demonstrate how the movement on the tetrahedron can be used for detecting unexpected trajectories, which might indicate transitions from health to disease and reveal abnormal conditions, even when all individual blood measurements are in the norm., Longitudinal multi-omics measurements are highly valuable in studying heterogeneity in health and disease phenotypes. Here, the authors apply Pareto Task Inference to analyze the clinical lab tests of 3094 individuals and find three wellness states, and one aberrant health state defining this cohort.

Background While Alzheimer disease (AD) and vascular dementia (VaD) may be accelerated by hypercholesterolemia, the mechanisms underlying this association are unclear. We tested whether dysregulation of cholesterol catabolism, through its conversion to primary bile acids (BAs), was associated with dementia pathogenesis. Methods and findings We used a 3-step study design to examine the role of the primary BAs, cholic acid (CA), and chenodeoxycholic acid (CDCA) as well as their principal biosynthetic precursor, 7α-hydroxycholesterol (7α-OHC), in dementia. In Step 1, we tested whether serum markers of cholesterol catabolism were associated with brain amyloid accumulation, white matter lesions (WMLs), and brain atrophy. In Step 2, we tested whether exposure to bile acid sequestrants (BAS) was associated with risk of dementia. In Step 3, we examined plausible mechanisms underlying these findings by testing whether brain levels of primary BAs and gene expression of their principal receptors are altered in AD. Step 1: We assayed serum concentrations CA, CDCA, and 7α-OHC and used linear regression and mixed effects models to test their associations with brain amyloid accumulation (N = 141), WMLs, and brain atrophy (N = 134) in the Baltimore Longitudinal Study of Aging (BLSA). The BLSA is an ongoing, community-based cohort study that began in 1958. Participants in the BLSA neuroimaging sample were approximately 46% male with a mean age of 76 years; longitudinal analyses included an average of 2.5 follow-up magnetic resonance imaging (MRI) visits. We used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (N = 1,666) to validate longitudinal neuroimaging results in BLSA. ADNI is an ongoing, community-based cohort study that began in 2003. Participants were approximately 55% male with a mean age of 74 years; longitudinal analyses included an average of 5.2 follow-up MRI visits. Lower serum concentrations of 7α-OHC, CA, and CDCA were associated with higher brain amyloid deposition (p = 0.041), faster WML accumulation (p = 0.050), and faster brain atrophy mainly (false discovery rate [FDR] p = 4 million registrants from general practice clinics in the United Kingdom, we tested whether patients using BAS (BAS users; 3,208 with ≥2 prescriptions), which reduce circulating BAs and increase cholesterol catabolism, had altered dementia risk compared to those on non-statin lipid-modifying therapies (LMT users; 23,483 with ≥2 prescriptions). Patients in the study (BAS/LMT) were approximately 34%/38% male and with a mean age of 65/68 years; follow-up time was 4.7/5.7 years. We found that BAS use was not significantly associated with risk of all-cause dementia (hazard ratio (HR) = 1.03, 95% confidence interval (CI) = 0.72–1.46, p = 0.88) or its subtypes. We found a significant difference between the risk of VaD in males compared to females (p = 0.040) and a significant dose–response relationship between BAS use and risk of VaD (p-trend = 0.045) in males. Step 3: We assayed brain tissue concentrations of CA and CDCA comparing AD and control (CON) samples in the BLSA autopsy cohort (N = 29). Participants in the BLSA autopsy cohort (AD/CON) were approximately 50%/77% male with a mean age of 87/82 years. We analyzed single-cell RNA sequencing (scRNA-Seq) data to compare brain BA receptor gene expression between AD and CON samples from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort (N = 46). ROSMAP is an ongoing, community-based cohort study that began in 1994. Participants (AD/CON) were approximately 56%/36% male with a mean age of 85/85 years. In BLSA, we found that CA and CDCA were detectable in postmortem brain tissue samples and were marginally higher in AD samples compared to CON. In ROSMAP, we found sex-specific differences in altered neuronal gene expression of BA receptors in AD. Study limitations include the small sample sizes in the BLSA cohort and likely inaccuracies in the clinical diagnosis of dementia subtypes in primary care settings. Conclusions We combined targeted metabolomics in serum and amyloid positron emission tomography (PET) and MRI of the brain with pharmacoepidemiologic analysis to implicate dysregulation of cholesterol catabolism in dementia pathogenesis. We observed that lower serum BA concentration mainly in males is associated with neuroimaging markers of dementia, and pharmacological lowering of BA levels may be associated with higher risk of VaD in males. We hypothesize that dysregulation of BA signaling pathways in the brain may represent a plausible biologic mechanism underlying these results. Together, our observations suggest a novel mechanism relating abnormalities in cholesterol catabolism to risk of dementia., Vijay Varma and co-workers study cholesterol and bile acid metabolism in dementia., Author summary Why was this study done? Hypercholesterolemia is associated with increased risk of Alzheimer disease (AD) and vascular dementia (VaD). However, cholesterol is impermeable to the blood–brain barrier (BBB), and it is unclear how peripheral cholesterol mediates risk of dementia. While prior research has examined the relationship between de novo cholesterol biosynthesis and dementia, few studies have assessed the role of cholesterol catabolism and its principal breakdown products, oxysterols, and primary bile acids (BAs) in dementia. What did the researchers do and find? We examined the role of cholesterol catabolism in dementia pathogenesis by first testing the association between serum oxysterols and BAs and neuroimaging markers of dementia. We also tested whether exposure to bile acid sequestrants (BAS) was associated with risk of dementia in a large, real-world clinical dataset. Finally, we tested plausible mechanisms underlying these associations by examining whether primary BAs and mRNA of their receptors were altered in the brain in dementia. We found that lower serum levels of 7α-hydroxycholesterol (7α-OHC) and primary BAs were associated with higher brain amyloid deposition, faster WML accumulation, and faster brain atrophy mainly in males. Consistent with this finding, we observed a sex difference in the association between use of BAS and risk of VaD. We found that primary BAs were detectable in the brain, and levels of gene expression of BA receptors were altered in AD mainly in males. What do these findings mean? Our findings suggest that cholesterol catabolism and BA synthesis may impact dementia progression through sex-specific effects on signaling pathways in the brain. These results set the stage for experimental studies to test whether BA signaling in the brain may be a novel therapeutic target in dementia.

Latent tuberculosis infection (LTBI) poses a major roadblock in the global effort to eradicate tuberculosis (TB). A deep understanding of the host responses involved in establishment and maintenance of TB latency is required to propel the development of sensitive methods to detect and treat LTBI. Given that LTBI individuals are typically asymptomatic, it is challenging to differentiate latently infected from uninfected individuals. A major contributor to this problem is that no clear pattern of host response is linked with LTBI, as molecular correlates of latent infection have been hard to identify. In this study, we have analyzed the global perturbations in host response in LTBI individuals as compared to uninfected individuals and particularly the heterogeneity in such response, across LTBI cohorts. For this, we constructed individualized genome-wide host response networks informed by blood transcriptomes for 136 LTBI cases and have used a sensitive network mining algorithm to identify top-ranked host response subnetworks in each case. Our analysis indicates that despite the high heterogeneity in the gene expression profiles among LTBI samples, clear patterns of perturbation are found in the immune response pathways, leading to grouping LTBI samples into 4 different immune-subtypes. Our results suggest that different subnetworks of molecular perturbations are associated with latent tuberculosis. © Copyright © 2021 Banerjee, Baloni, Singh and Chandra.

The immunological picture of how different patients recover from COVID-19, and how those recovery trajectories are influenced by infection severity, remain unclear. We investigated 140 COVID-19 patients from diagnosis to convalescence using clinical data, viral load assessments, and multi-omic analyses of blood plasma and circulating immune cells. Immune-phenotype dynamics resolved four recovery trajectories. One trajectory signals a return to pre-infection healthy baseline, while the other three are characterized by differing fractions of persistent cytotoxic and proliferative T cells, distinct B cell maturation processes, and memory-like innate immunity. We resolve a small panel of plasma proteins that, when measured at diagnosis, can predict patient survival and recovery-trajectory commitment. Our study offers novel insights into post-acute immunological outcomes of COVID-19 that likely influence long-term adverse sequelae.

Aggregation and accumulation of amyloid-β (Aβ) is a defining feature of Alzheimer’s disease (AD) pathology. To study microglial responses to Aβ, we applied exogenous Aβ peptide, in either oligomeric or fibrillar conformation, to primary mouse microglial cultures and evaluated system level transcriptional changes and then compared these to transcriptomic changes in the brains of CRND8 APP mice. We find that primary microglial cultures have rapid and massive transcriptional change to in response to Aβ. Transcriptomic responses to oligomeric or fibrillar Aβ in primary microglia, though partially overlapping, are distinct and are not recapitulated in vivo where Aβ progressively accumulates. Furthermore, though classic immune mediators show massive transcriptional changes in the primary microglial cultures, these changes are not observed in the mouse model. Together, these data extend previous studies which demonstrate that microglia responses ex vivo are poor proxies for in vivo responses. Finally, these data demonstrate the potential utility of using microglia as biosensors of different aggregate conformation, as the transcriptional responses to oligomeric and fibrillar Aβ can be distinguished.

Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

Author(s): Su, Yapeng; Chen, Daniel; Lausted, Christopher; Yuan, Dan; Choi, Jongchan; Dai, Cheng; Voillet, Valentin; Scherler, Kelsey; Troisch, Pamela; Duvvuri, Venkata R; Baloni, Priyanka; Qin, Guangrong; Smith, Brett; Kornilov, Sergey; Rostomily, Clifford; Xu, Alex; Li, Jing; Dong, Shen; Rothchild, Alissa; Zhou, Jing; Murray, Kim; Edmark, Rick; Hong, Sunga; Jones, Lesley; Zhou, Yong; Roper, Ryan; Mackay, Sean; O'Mahony, D Shane; Dale, Christopher R; Wallick, Julie A; Algren, Heather A; Michael, Zager A; Magis, Andrew; Wei, Wei; Price, Nathan D; Huang, Sui; Subramanian, Naeha; Wang, Kai; Hadlock, Jennifer; Hood, Leroy; Aderem, Alan; Bluestone, Jeffrey A; Lanier, Lewis L; Greenberg, Phil; Gottardo, Raphael; Davis, Mark M; Goldman, Jason D; Heath, James R | Abstract: Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8 + and CD4 + T cells, and cytotoxic CD4 + T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

Summary Metastatic colorectal cancer (CRC) is a major cause of cancer-related death, and incidence is rising in younger populations (younger than 50 years). Current chemotherapies can achieve response rates above 50%, but immunotherapies have limited value for patients with microsatellite-stable (MSS) cancers. The present study investigates the impact of chemotherapy on the tumor immune microenvironment. We treat human liver metastases slices with 5-fluorouracil (5-FU) plus either irinotecan or oxaliplatin, then perform single-cell transcriptome analyses. Results from eight cases reveal two cellular subtypes with divergent responses to chemotherapy. Susceptible tumors are characterized by a stemness signature, an activated interferon pathway, and suppression of PD-1 ligands in response to 5-FU+irinotecan. Conversely, immune checkpoint TIM-3 ligands are maintained or upregulated by chemotherapy in CRC with an enterocyte-like signature, and combining chemotherapy with TIM-3 blockade leads to synergistic tumor killing. Our analyses highlight chemomodulation of the immune microenvironment and provide a framework for combined chemo-immunotherapies., Graphical Abstract, Highlights CRLM slice culture can assess immune response to chemotherapy Single-cell analysis identifies cancer subtypes with differing response to chemotherapy 5-FU+irinotecan modulates interferon and PD-L1 pathways in stem-like CRLM Combining chemotherapy with TIM-3 blockade is synergistic in enterocyte-like CRLM, The response of colorectal liver metastases (CRLM) to chemotherapy is analyzed in 3D organotypic tumor slices by single-cell RNA-seq. Jabbari et al. find two subtypes (stem-like and enterocyte-like) of CRLM that express different immune checkpoint ligands and respond differently to chemotherapy. The study highlights the chemomodulation of the tumor immune microenvironment that can be targeted therapeutically.

Alzheimer’s disease (AD) is the leading cause of dementia, with metabolic dysfunction seen years before the emergence of clinical symptoms. Increasing evidence suggests a role for primary and secondary bile acids, the end-product of cholesterol metabolism, influencing pathophysiology in AD. In this study, we analyzed transcriptomes from 2114 post-mortem brain samples from three independent cohorts and identified that the genes involved in the alternative bile acid synthesis pathway were expressed in the brain compared to the classical pathway. These results were supported by targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals. We reconstructed brain region-specific metabolic networks using data from three independent cohorts to assess the role of bile acid metabolism in AD pathophysiology. Our metabolic network analysis suggested that taurine transport, bile acid synthesis and cholesterol metabolism differed in AD and cognitively normal individuals. Using the brain transcriptional regulatory network, we identified putative transcription factors regulating these metabolic genes and influencing altered metabolism in AD. Intriguingly, we find bile acids from the brain metabolomics whose synthesis cannot be explained by enzymes we find in the brain, suggesting they may originate from an external source such as the gut microbiome. These findings motivate further research into bile acid metabolism and transport in AD to elucidate their possible connection to cognitive decline.

Alzheimer’s disease (AD) is the leading cause of dementia, with metabolic dysfunction seen years before the emergence of clinical symptoms. Increasing evidence suggests a role for primary and secondary bile acids, the end-product of cholesterol metabolism, influencing pathophysiology in AD. In this study, we analyzed transcriptomes from 2114 post-mortem brain samples from three independent cohorts and identified that the genes involved in alternative bile acid synthesis pathway was expressed in brain compared to the classical pathway. These results were supported by targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals. We reconstructed brain region-specific metabolic networks using data from three independent cohorts to assess the role of bile acid metabolism in AD pathophysiology. Our metabolic network analysis suggested that taurine transport, bile acid synthesis and cholesterol metabolism differed in AD and cognitively normal individuals. Using the brain transcriptional regulatory network, we identified putative transcription factors regulating these metabolic genes and influencing altered metabolism in AD. Intriguingly, we find bile acids from the brain metabolomics whose synthesis cannot be explained by enzymes we find in the brain, suggesting they may originate from an external source such as the gut microbiome. These findings motivate further research into bile acid metabolism and transport in AD to elucidate their possible connection to cognitive decline.

Mapping network analysis in cells and tissues can provide insights into metabolic adaptations to changes in external environment, pathological conditions, and nutrient deprivation. Here, we reconstructed a genome-scale metabolic network of the rat liver that will allow for exploration of systems-level physiology. The resulting in silico model (iRatLiver) contains 1,882 reactions, 1,448 metabolites, and 994 metabolic genes. We then used this model to characterize the response of the liver’s energy metabolism to a controlled perturbation in diet. Transcriptomics data were collected from the livers of Sprague Dawley rats at 4 or 14 days of being subjected to 15%, 30%, or 60% diet restriction. These data were integrated with the iRatLiver model to generate condition-specific metabolic models, allowing us to explore network differences under each condition. We observed different pathway usage between early and late time points. Network analysis identified several highly connected “hub” genes (Pklr, Hadha, Tkt, Pgm1, Tpi1, and Eno3) that showed differing trends between early and late time points. Taken together, our results suggest that the liver’s response varied with short- and long-term diet restriction. More broadly, we anticipate that the iRatLiver model can be exploited further to study metabolic changes in the liver under other conditions such as drug treatment, infection, and disease.

Exogenous amyloid beta peptides cause distinct transcriptomic responses in microglia cultures that are not fully replicated in an amyloid mouse model or identified microglial subpopulations in vivo., Aggregation and accumulation of amyloid-β (Aβ) is a defining feature of Alzheimer’s disease pathology. To study microglial responses to Aβ, we applied exogenous Aβ peptide, in either oligomeric or fibrillar conformation, to primary mouse microglial cultures and evaluated system-level transcriptional changes and then compared these with transcriptomic changes in the brains of CRND8 APP mice. We find that primary microglial cultures have rapid and massive transcriptional change in response to Aβ. Transcriptomic responses to oligomeric or fibrillar Aβ in primary microglia, although partially overlapping, are distinct and are not recapitulated in vivo where Aβ progressively accumulates. Furthermore, although classic immune mediators show massive transcriptional changes in the primary microglial cultures, these changes are not observed in the mouse model. Together, these data extend previous studies which demonstrate that microglia responses ex vivo are poor proxies for in vivo responses. Finally, these data demonstrate the potential utility of using microglia as biosensors of different aggregate conformation, as the transcriptional responses to oligomeric and fibrillar Aβ can be distinguished.

The global mechanisms and associated molecular alterations that occur in drug-resistant mycobacteria are poorly understood. To address this, we obtain genomics data and then construct a genome-scale response network in isoniazid-resistant Mycobacterium smegmatis and apply a network-mining algorithm. Through this, we decipher global alterations in an unbiased manner and identify emergent vulnerabilities in resistant bacilli, of which redox response was prominent. Using phenotypic profiling, we find that resistant bacilli exhibit collateral sensitivity to several compounds that block antioxidant responses. We find that nanogram/milliliter concentrations of ebselen, vancomycin, and phenylarsine oxide, in combination with isoniazid, are highly effective against Mycobacterium tuberculosis H37Rv and three clinical drug-resistant strains. Dynamic measurements of cytoplasmic redox potential revealed a surprisingly diminished capacity of clinical drug-resistant strains to counteract oxidative stress, providing a mechanistic basis for efficient and synergistic mycobactericidal activity of the drug combinations. Ebselen and vancomycin appear to be promising repurposable drugs.

We present an integrated analysis of the clinical measurements, immune cells and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention., Highlights • Analysis of serial blood from 139 COVID-19 patients reveals immune coordination • A major immunological shift is seen between mild and moderate infection • Moderate and severe cases exhibit inflammation and a sharp drop in blood nutrients • Novel immune cell subsets emerge in moderate cases and increase with severity, Using serial blood draws from hospitalized COVID-19 patients, Su et al. present an extensive single-cell multi-omics dataset covering the first week infection following clinical diagnosis, which includes information on plasma proteins, metabolites, transcriptomic data, immune receptor sequences, secreted proteins, and electronic health record data. Their integrated analysis identifies a major immunological shift between mild and moderate infection, which includes an increase in inflammation, drop in blood nutrients, and the emergence of novel immune cell subpopulations that intensify with disease severity.

Summary Increasing evidence suggests Alzheimer's disease (AD) pathophysiology is influenced by primary and secondary bile acids, the end product of cholesterol metabolism. We analyze 2,114 post-mortem brain transcriptomes and identify genes in the alternative bile acid synthesis pathway to be expressed in the brain. A targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals supports these results. Our metabolic network analysis suggests that taurine transport, bile acid synthesis, and cholesterol metabolism differ in AD and cognitively normal individuals. We also identify putative transcription factors regulating metabolic genes and influencing altered metabolism in AD. Intriguingly, some bile acids measured in brain tissue cannot be explained by the presence of enzymes responsible for their synthesis, suggesting that they may originate from the gut microbiome and are transported to the brain. These findings motivate further research into bile acid metabolism in AD to elucidate their possible connection to cognitive decline., Graphical Abstract, Highlights Altered cholesterol and bile acid metabolism linked to Alzheimer disease (AD) Evidence for alternative bile acid pathway genes expression in post-mortem brains Metabolomics analysis shows secondary bile acids associated with cognitive decline Genome-scale metabolic networks of brain regions identify reactions linked to AD, Baloni et al. use a systems biology approach to identify alterations in cholesterol and bile acid metabolism in Alzheimer disease (AD). Expression of alternative bile acid and neural cholesterol clearance pathway along with transporters of taurine and bile acids suggest the role of the gut-brain axis in AD.

The global variations in the gene expression pattern of drug treated (½X) and laboratory evolved drug-resistant strains (2XR and 4XR) of Mycobacterium smegmatis were obtained and compared with the M. smegmatis mc2 155 (WT) strain. The genes exhibiting two-fold change and p-value

Mycobacterium tuberculosis (Mtb) expresses a broad-spectrum β-lactamase (BlaC) that mediates resistance to one of the highly effective antibacterials, β-lactams. Nonetheless, β-lactams showed mycobactericidal activity in combination with β-lactamase inhibitor, clavulanate (Clav). However, the mechanistic aspects of how Mtb responds to β-lactams such as Amoxicillin in combination with Clav (referred as Augmentin [AG]) are not clear. Here, we identified cytoplasmic redox potential and intracellular redox sensor, WhiB4, as key determinants of mycobacterial resistance against AG. Using computer-based, biochemical, redox-biosensor, and genetic strategies, we uncovered a functional linkage between specific determinants of β-lactam resistance (e.g. β-lactamase) and redox potential in Mtb. We also describe the role of WhiB4 in coordinating the activity of β-lactamase in a redox-dependent manner to tolerate AG. Disruption of WhiB4 enhances AG tolerance, whereas overexpression potentiates AG activity against drug-resistant Mtb. Our findings suggest that AG can be exploited to diminish drug-resistance in Mtb through redox-based interventions. DOI: http://dx.doi.org/10.7554/eLife.25624.001, eLife digest A bacterium called Mycobacterium tuberculosis causes tuberculosis in humans. Multiple antibiotics are available to treat this infection, yet around one million people still die from tuberculosis each year. One of the reasons that the number of deaths is so high is because many M. tuberculosis cells have become resistant to these drugs. Therefore, new drug treatments are urgently needed to tackle the disease. When cells are under stress – for example, when a bacterial cell is exposed to an antibiotic – they can increase the production of chemicals known as reactive oxygen species. These chemicals are vital to many processes in cells, but if their levels get too high they can kill cells by damaging DNA and other molecules. To prevent this damage, bacterial cells produce molecules, such as mycothiol, to neutralize the excess reactive oxygen species. A therapy called Augmentin is used to fight many different types of bacterial infection. It combines an antibiotic known as amoxicillin with another drug that blocks the activity of a bacterial enzyme responsible for breaking down amoxicillin-like drugs. Augmentin can also kill M. tuberculosis cells, but it was not clear exactly how it works, or how the bacteria might be able to develop resistance to this treatment. Here, Mishra et al. combined a computational technique known as network analysis with experiments to study the affect of Augmentin on M. tuberculosis. The experiments reveal that M. tuberculosis cells can develop resistance to Augmentin by increasing the production of an enzyme that breaks down the antibiotic and by neutralizing reactive oxygen species with help of mycothiol. Augmentin treatment can decrease the production of a protein called WhiB4 in the bacteria. This protein is involved in detecting when cells are stressed and regulates the levels of both mycothiol and the enzyme that breaks down the antibiotic. Increasing the production of this protein made the bacterial cells more susceptible to Augmentin treatment by decreasing the levels of active mycothiol and reducing the production of the enzyme that breaks down the antibiotic drug. These findings suggest that Augmentin could be more effective against drug-resistant tuberculosis and other bacterial infections if it is combined with a drug that can alter the levels of reactive oxygen species inside bacterial cells. The next step is to search for new molecules that may be able to perform such a role. DOI: http://dx.doi.org/10.7554/eLife.25624.002

Tuberculosis remains a major global health challenge worldwide, causing more than a million deaths annually. To determine newer methods for detecting and combating the disease, it is necessary to characterise global host responses to infection. Several high throughput omics studies have provided a rich resource including a list of several genes differentially regulated in tuberculosis. An integrated analysis of these studies is necessary to identify a unified response to the infection. Such data integration is met with several challenges owing to platform dependency, patient heterogeneity, and variability in the extent of infection, resulting in little overlap among different datasets. Network-based approaches offer newer alternatives to integrate and compare diverse data. In this study, we describe a meta-analysis of host’s whole blood transcriptomic profiles that were integrated into a genome-scale protein–protein interaction network to generate response networks in active tuberculosis, and monitor their behaviour over treatment. We report the emergence of a highly active common core in disease, showing partial reversals upon treatment. The core comprises 380 genes in which STAT1, phospholipid scramblase 1 (PLSCR1), C1QB, OAS1, GBP2 and PSMB9 are prominent hubs. This network captures the interplay between several biological processes including pro-inflammatory responses, apoptosis, complement signalling, cytoskeletal rearrangement, and enhanced cytokine and chemokine signalling. The common core is specific to tuberculosis, and was validated on an independent dataset from an Indian cohort. A network-based approach thus enables the identification of common regulators that characterise the molecular response to infection, providing a platform-independent foundation to leverage maximum insights from available clinical data., Tuberculosis: an underlying common-core host response network Patients suffering from tuberculosis (TB) show a high extent of variations in their gene expression patterns. Such heterogeneity poses major road blocks to our understanding of how hosts respond to the disease. A number of studies have profiled transcriptomes of human blood samples from TB patients, but a meta-analysis indicates that very few changes are consistently seen. The problem, to a large extent, lies with the way large data is analysed. We have used a genome-wide network approach to characterise the host response and have identified a common-core in the TB response networks of different patients, indicating the presence of unified host response mechanisms. This core network provides a comprehensive view into the most significant regulators of the infection-mediated biological processes across patients from different populations, and it shows partial reversals upon treatment.

Transcriptional regulation enables adaptation in bacteria. Typically, only a few transcriptional events are well understood, leaving many others unidentified. The recent genome-wide identification of transcription factor binding sites in Mycobacterium tuberculosis has changed this by deciphering a molecular road-map of transcriptional control, indicating active events and their immediate downstream effects.

Metabolism forms an integral part of all cells and its study is important to understand the functioning of the system, to understand alterations that occur in disease state and hence for subsequent applications in drug discovery. Reconstruction of genome-scale metabolic graphs from genomics and other molecular or biochemical data is now feasible. Few methods have also been reported for inferring biochemical pathways from these networks. However, given the large scale and complex inter-connections in the networks, the problem of identifying biochemical routes is not trivial and some questions still remain open. In particular, how a given path is altered in perturbed conditions remains a difficult problem, warranting development of improved methods. Here we report a comparison of 6 different weighting schemes to derive node and edge weights for a metabolic graph, weights reflecting various kinetic, thermodynamic parameters as well as abundances inferred from transcriptome data. Using a network of 50 nodes and 107 edges of carbohydrate metabolism, we show that kinetic parameter derived weighting schemes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left[ {\left( {\frac{{K_{M}^{S} }}{{ K_{M}^{P } }}} \right){\text{ and }}\left( { \frac{{K_{M} }}{{K_{cat} }} } \right)} \right]$$\end{document} fare best. However, these are limited by their extent of availability, highlighting the usefulness of omics data under such conditions. Interestingly, transcriptome derived weights yield paths with best scores, but are inadequate to discriminate the theoretical paths. The method is tested on a system of Escherichia coli stress response. The approach illustrated here is generic in nature and can be used in the analysis for metabolic network from any species and perhaps more importantly for comparing condition-specific networks.

AimsInhibition of β-lactamase by clavulanate (Clav) sensitizes multi-and extensively drug-resistant Mycobacterium tuberculosis (Mtb) strains towards β-lactams such as amoxicillin (Amox). However, the underlying mechanism of how Mtb responds to Amox-Clav combination (Augmentin; AG) is not characterized.ResultsWe integrated global expression profiling with the protein-protein interaction landscape and generated a genome-scale network of Mtb in response to AG. In addition to specific targets (e.g., peptidoglycan biosynthesis and β-lactamase), the response to AG was also centered on redox-balance, central carbon metabolism (CCM), and respiration in Mtb. We discovered that AG modulates superoxide levels, NADH/NAD+ balance and mycothiol redox potential (EMSH) of Mtb. Higher intra-mycobacterial EMSH potentiates mycobactericidal efficacy of AG, whereas lower EMSH induces tolerance. Further, Mtb responds to AG via a redox-sensitive transcription factor, WhiB4. MtbΔwhiB4 displayed higher expression of genes involved in β-lactam resistance along with those mediating respiration, CCM and redox balance. Moreimportantly, WhiB4 binds to the promoter regions and represses transcription of genes involved in β-lactamase expression in a redox-dependent manner. Lastly, while MtbΔwhiB4 maintained internal EMSH, exhibited greater β-lactamase activity and displayed AG-tolerance, overexpression of WhiB4 induced oxidative shift in EMSH and repressed β-lactamase activity to aggravate AG-mediated killing of drug-sensitive and –resistant strains of Mtb.Innovation and ConclusionsThis work demonstrate that efficacy of β-lactam\β-lactamase inhibitor combination can be attenuated by elevating mycobacterial antioxidant capabilities and potentiated by impairing redox buffering capacity of Mtb. The functional linkage between β-lactams, redox balance, and WhiB4 can be exploited to potentiate AG action against drug-resistant Mtb.

The alarmone (p)ppGpp regulates transcription, translation, replication, virulence, lipid synthesis, antibiotic sensitivity, biofilm formation, and other functions in bacteria. Signaling nucleotide cyclic di-GMP (c-di-GMP) regulates biofilm formation, motility, virulence, the cell cycle, and other functions. In Mycobacterium smegmatis , both (p)ppGpp and c-di-GMP are synthesized and degraded by bifunctional proteins Rel Msm and DcpA, encoded by rel Msm and dcpA genes, respectively. We have previously shown that the Δ rel Msm and Δ dcpA knockout strains are antibiotic resistant and defective in biofilm formation, show altered cell surface properties, and have reduced levels of glycopeptidolipids and polar lipids in their cell wall (K. R. Gupta, S. Kasetty, and D. Chatterji, Appl Environ Microbiol 81:2571–2578, 2015, http://dx.doi.org/10.1128/AEM.03999-14 ). In this work, we have explored the phenotypes that are affected by both (p)ppGpp and c-di-GMP in mycobacteria. We have shown that both (p)ppGpp and c-di-GMP are needed to maintain the proper growth rate under stress conditions such as carbon deprivation and cold shock. Scanning electron microscopy showed that low levels of these second messengers result in elongated cells, while high levels reduce the cell length and embed the cells in a biofilm-like matrix. Fluorescence microscopy revealed that the elongated Δ rel Msm and Δ dcpA cells are multinucleate, while transmission electron microscopy showed that the elongated cells are multiseptate. Gene expression analysis also showed that genes belonging to functional categories such as virulence, detoxification, lipid metabolism, and cell-wall-related processes were differentially expressed. Our results suggests that both (p)ppGpp and c-di-GMP affect some common phenotypes in M. smegmatis , thus raising a possibility of cross talk between these two second messengers in mycobacteria. IMPORTANCE Our work has expanded the horizon of (p)ppGpp and c-di-GMP signaling in Gram-positive bacteria. We have come across a novel observation that M. smegmatis needs (p)ppGpp and c-di-GMP for cold tolerance. We had previously shown that the Δ rel Msm and Δ dcpA strains are defective in biofilm formation. In this work, the overproduction of (p)ppGpp and c-di-GMP encased M. smegmatis in a biofilm-like matrix, which shows that both (p)ppGpp and c-di-GMP are needed for biofilm formation. The regulation of cell length and cell division by (p)ppGpp was known in mycobacteria, but our work shows that c-di-GMP also affects the cell size and cell division in mycobacteria. This is perhaps the first report of c-di-GMP regulating cell division in mycobacteria.

We report the whole genome sequences of a Mycobacterium smegmatis laboratory wild-type strain (MC 2 155) and mutants (4XR1, 4XR2) resistant to isoniazid. Compared to Mycobacterium smegmatis MC 2 155 (NC_008596), a widely used strain in laboratory experiments, the MC 2 155, 4XR1, and 4XR2 strains are 60, 128 and 93 bp longer, respectively.

Exposure to infectious agents can either lead to active disease or containment or killing of the pathogen. Outcome of an infectious disease is determined by the complex interplay between the host and the pathogen. Therefore, understanding the crosstalk between the host and the pathogen during infection is crucial to identify molecules that are important for the spread or suppression of the disease and for identification of drug targets. Both the host and the pathogen have several mechanisms for countering each other thereby adding layers of complexity to the host-pathogen interplay. Reconstructing mathematical models of complex processes such as cell regulations, signal transductions and host-pathogen interactions provide a detailed understanding of the various interactions and crosstalks occuring in a biological system and thus form a platform to study the system as a whole. Various experimental methods in functional genomics and proteomics as well as computational approaches have been developed over the years that help in building and modeling the biological systems. These approaches have proved quite helpful in identifying drug targets, generating hypotheses rationalizing and finally predicting the cause and final outcome of diseases.

Background The set of indispensable genes that are required by an organism to grow and sustain life are termed as essential genes. There is a strong interest in identification of the set of essential genes, particularly in pathogens, not only for a better understanding of the pathogen biology, but also for identifying drug targets and the minimal gene set for the organism. Essentiality is inherently a systems property and requires consideration of the system as a whole for their identification. The available experimental approaches capture some aspects but each method comes with its own limitations. Moreover, they do not explain the basis for essentiality in most cases. A powerful prediction method to recognize this gene pool including rationalization of the known essential genes in a given organism would be very useful. Here we describe a multi-level multi-scale approach to identify the essential gene pool in a deadly pathogen, Mycobacterium tuberculosis. Results The multi-level workflow analyses the bacterial cell by studying (a) genome-wide gene expression profiles to identify the set of genes which show consistent and significant levels of expression in multiple samples of the same condition, (b) indispensability for growth by using gene expression integrated flux balance analysis of a genome-scale metabolic model, (c) importance for maintaining the integrity and flow in a protein-protein interaction network and (d) evolutionary conservation in a set of genomes of the same ecological niche. In the gene pool identified, the functional basis for essentiality has been addressed by studying residue level conservation and the sub-structure at the ligand binding pockets, from which essential amino acid residues in that pocket have also been identified. 283 genes were identified as essential genes with high-confidence. An agreement of about 73.5% is observed with that obtained from the experimental transposon mutagenesis technique. A large proportion of the identified genes belong to the class of intermediary metabolism and respiration. Conclusions The multi-scale, multi-level approach described can be generally applied to other pathogens as well. The essential gene pool identified form a basis for designing experiments to probe their finer functional roles and also serve as a ready shortlist for identifying drug targets.

Background Many studies on M. tuberculosis have emerged from using M. smegmatis MC2155 (Msm), since they share significant similarities and yet Msm is non-pathogenic and faster growing. Although several individual molecules have been studied from Msm, many questions remain open about its metabolism as a whole and its capability to be versatile. Adaptability and versatility are emergent properties of a system, warranting a molecular systems perspective to understand them. Results We identify feasible metabolic pathways in Msm in reference condition with transcriptome, phenotypic microarray, along with functional annotation of the genome. Together with transcriptome data, specific genes from a set of alternatives have been mapped onto different pathways. About 257 metabolic pathways can be considered to be feasible in Msm. Next, we probe cellular metabolism with an array of alternative carbon and nitrogen sources and identify those that are utilized and favour growth as well as those that do not support growth. In all, about 135 points in the entire metabolic map are probed. Analyzing growth patterns under these conditions, lead us to hypothesize different pathways that can become active in various conditions and possible alternate routes that may be induced, thus explaining the observed physiological adaptations. Conclusions The study provides the first detailed analysis of feasible pathways towards adaptability. We obtain mechanistic insights that explain observed phenotypic behaviour by studying gene-expression profiles and pathways inferred from the genome sequence. Comparison of transcriptome and phenome analysis of Msm and Mtb provides a rationale for understanding commonalities in metabolic adaptability. Electronic supplementary material The online version of this article (doi:10.1186/s12866-014-0276-5) contains supplementary material, which is available to authorized users.

Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk-factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data, and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific autoantibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies., By correlating patient symptoms with in-depth profiling of blood cells and plasma components throughout COVID-19 infection, this study identifies factors that may predict sustained disease.